315 results on '"KENNEDY, CHRIS J."'
Search Results
2. Correction: The stories about racism and health: the development of a framework for racism narratives in medical literature using a computational grounded theory approach
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Figueroa, Caroline A., Manalo-Pedro, Erin, Pola, Swetha, Darwish, Sajia, Sachdeva, Pratik, Guerrero, Christian, von Vacano, Claudia, Jha, Maithili, De Maio, Fernando, and Kennedy, Chris J.
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- 2024
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3. Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities
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Nguyen, Thu T, Yu, Weijun, Merchant, Junaid S, Criss, Shaniece, Kennedy, Chris J, Mane, Heran, Gowda, Krishik N, Kim, Melanie, Belani, Ritu, Blanco, Caitlin F, Kalachagari, Manvitha, Yue, Xiaohe, Volpe, Vanessa V, Allen, Amani M, Hswen, Yulin, and Nguyen, Quynh C
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Creative Arts and Writing ,Communication and Media Studies ,Language ,Communication and Culture ,Screen and Digital Media ,Good Health and Well Being ,Humans ,Hate ,Mass Media ,Social Media ,Speech ,United States ,Racism ,machine learning ,news media ,racial health disparities ,social media ,Toxicology - Abstract
We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.
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- 2023
4. A real-time spatiotemporal AI model analyzes skill in open surgical videos
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Goodman, Emmett D., Patel, Krishna K., Zhang, Yilun, Locke, William, Kennedy, Chris J., Mehrotra, Rohan, Ren, Stephen, Guan, Melody Y., Downing, Maren, Chen, Hao Wei, Clark, Jevin Z., Brat, Gabriel A., and Yeung, Serena
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (AI) has the potential to optimize surgical practice and improve patient outcomes, but efforts have focused primarily on minimally invasive techniques. Our work overcomes existing data limitations for training AI models by curating, from YouTube, the largest dataset of open surgical videos to date: 1997 videos from 23 surgical procedures uploaded from 50 countries. Using this dataset, we developed a multi-task AI model capable of real-time understanding of surgical behaviors, hands, and tools - the building blocks of procedural flow and surgeon skill. We show that our model generalizes across diverse surgery types and environments. Illustrating this generalizability, we directly applied our YouTube-trained model to analyze open surgeries prospectively collected at an academic medical center and identified kinematic descriptors of surgical skill related to efficiency of hand motion. Our Annotated Videos of Open Surgery (AVOS) dataset and trained model will be made available for further development of surgical AI., Comment: 22 pages, 4 main text figures, 7 extended data figures, 4 extended data tables
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- 2021
5. Assessing Annotator Identity Sensitivity via Item Response Theory: A Case Study in a Hate Speech Corpus
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Sachdeva, Pratik S, Barreto, Renata, von Vacano, Claudia, and Kennedy, Chris J
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- 2022
6. Comparative effectiveness of remote digital gamified and group CBT skills training interventions for anxiety and depression among college students: Results of a three-arm randomised controlled trial
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Bantjes, Jason, Hunt, Xanthe, Cuijpers, Pim, Kazdin, Alan E., Kennedy, Chris J., Luedtke, Alex, Malenica, Ivana, Petukhova, Maria, Sampson, Nancy, Zainal, Nur Hani, Davids, Charl, Dunn-Coetzee, Munita, Gerber, Rone, Stein, Dan J., and Kessler, Ronald C.
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- 2024
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7. Predicting Homelessness Among Transitioning U.S. Army Soldiers
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Tsai, Jack, Szymkowiak, Dorota, Hooshyar, Dina, Gildea, Sarah M., Hwang, Irving, Kennedy, Chris J., King, Andrew J., Koh, Katherine A., Luedtke, Alex, Marx, Brian P., Montgomery, Ann E., O'Brien, Robert W., Petukhova, Maria V., Sampson, Nancy A., Stein, Murray B., Ursano, Robert J., and Kessler, Ronald C.
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- 2024
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8. The stories about racism and health: the development of a framework for racism narratives in medical literature using a computational grounded theory approach
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Figueroa, Caroline A., Manalo-Pedro, Erin, Pola, Swetha, Darwish, Sajia, Sachdeva, Pratik, Guerrero, Christian, von Vacano, Claudia, Jha, Maithili, De Maio, Fernando, and Kennedy, Chris J.
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- 2023
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9. Evaluation of a city-wide school-located influenza vaccination program in Oakland, California with respect to race and ethnicity: A matched cohort study.
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Nguyen, Anna T, Arnold, Benjamin F, Kennedy, Chris J, Mishra, Kunal, Pokpongkiat, Nolan N, Seth, Anmol, Djajadi, Stephanie, Holbrook, Kate, Pan, Erica, Kirley, Pam D, Libby, Tanya, Hubbard, Alan E, Reingold, Arthur, Colford, John M, and Benjamin-Chung, Jade
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Humans ,Influenza Vaccines ,Vaccination ,Cohort Studies ,Cross-Sectional Studies ,Schools ,Aged ,Child ,School Health Services ,California ,Influenza ,Human ,Ethnicity ,Vaccine Efficacy ,Hospitalizations ,Influenza ,School-located influenza vaccinations ,Vaccination coverage ,Vaccinations ,Immunization ,Clinical Research ,Pneumonia & Influenza ,Vaccine Related ,Emerging Infectious Diseases ,Prevention ,Biological Sciences ,Agricultural and Veterinary Sciences ,Medical and Health Sciences ,Virology - Abstract
BackgroundIncreasing influenza vaccination coverage in school-aged children may substantially reduce community transmission. School-located influenza vaccinations (SLIV) aim to promote vaccinations by increasing accessibility, which may be especially beneficial to race/ethnicity groups that face high barriers to preventative care. Here, we evaluate the effectiveness of a city-wide SLIV program by race/ethnicity from 2014 to 2018.MethodsWe used multivariate matching to pair schools in the intervention district in Oakland, CA with schools in a comparison district in West Contra Costa County, CA. We distributed cross-sectional surveys to measure caregiver-reported student vaccination status and estimated differences in vaccination coverage levels and reasons for non-vaccination between districts stratifying by race/ethnicity. We estimated difference-in-differences (DID) of laboratory confirmed influenza hospitalization incidence between districts stratified by race/ethnicity using surveillance data.ResultsDifferences in influenza vaccination coverage in the intervention vs. comparison district were larger among White (2017-18: 21.0% difference [95% CI: 9.7%, 32.3%]) and Hispanic/Latino (13.4% [8.8%, 18.0%]) students than Asian/Pacific Islander (API) (8.9% [1.3%, 16.5%]), Black (5.9% [-2.2%, 14.0%]), and multiracial (6.3% [-1.8%, 14.3%)) students. Concerns about vaccine effectiveness or safety were more common among Black and multiracial caregivers. Logistical barriers were less common in the intervention vs. comparison district, with the largest difference among White students. In both districts, hospitalizations in 2017-18 were higher in Blacks (Intervention: 111.5 hospitalizations per 100,00; Comparison: 134.1 per 100,000) vs. other races/ethnicities. All-age influenza hospitalization incidence was lower in the intervention site vs. comparison site among White/API individuals in 2016-17 (DID -25.14 per 100,000 [95% CI: -40.14, -10.14]) and 2017-18 (-36.6 per 100,000 [-52.7, -20.5]) and Black older adults in 2017-18 (-282.2 per 100,000 (-508.4, -56.1]), but not in other groups.ConclusionsSLIV was associated with higher vaccination coverage and lower influenza hospitalization, but associations varied by race/ethnicity. SLIV alone may be insufficient to ensure equitable influenza outcomes.
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- 2022
10. Tracking e-cigarette warning label compliance on Instagram with deep learning
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Kennedy, Chris J., Vassey, Julia, Chang, Ho-Chun Herbert, Unger, Jennifer B., and Ferrara, Emilio
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Computer Science - Social and Information Networks ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The U.S. Food & Drug Administration (FDA) requires that e-cigarette advertisements include a prominent warning label that reminds consumers that nicotine is addictive. However, the high volume of vaping-related posts on social media makes compliance auditing expensive and time-consuming, suggesting that an automated, scalable method is needed. We sought to develop and evaluate a deep learning system designed to automatically determine if an Instagram post promotes vaping, and if so, if an FDA-compliant warning label was included or if a non-compliant warning label was visible in the image. We compiled and labeled a dataset of 4,363 Instagram images, of which 44% were vaping-related, 3% contained FDA-compliant warning labels, and 4% contained non-compliant labels. Using a 20% test set for evaluation, we tested multiple neural network variations: image processing backbone model (Inceptionv3, ResNet50, EfficientNet), data augmentation, progressive layer unfreezing, output bias initialization designed for class imbalance, and multitask learning. Our final model achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on vaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94 [97%] on non-compliant warning labels. We conclude that deep learning models can effectively identify vaping posts on Instagram and track compliance with FDA warning label requirements., Comment: 9 pages, 3 figures
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- 2021
11. City-wide school-located influenza vaccination: A retrospective cohort study.
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Benjamin-Chung, Jade, Arnold, Benjamin F, Mishra, Kunal, Kennedy, Chris J, Nguyen, Anna, Pokpongkiat, Nolan N, Djajadi, Stephanie, Seth, Anmol, Klein, Nicola P, Hubbard, Alan E, Reingold, Arthur, and Colford, John M
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Humans ,Influenza Vaccines ,Vaccination ,Retrospective Studies ,Schools ,Aged ,Child ,School Health Services ,Influenza ,Human ,Influenza ,Influenza vaccination ,School-located influenza vaccination ,Vaccine Related ,Pediatric ,Pneumonia & Influenza ,Emerging Infectious Diseases ,Clinical Research ,Prevention ,Infectious Diseases ,Biological Sciences ,Agricultural and Veterinary Sciences ,Medical and Health Sciences ,Virology - Abstract
BackgroundWe measured the effectiveness of a city-wide school-located influenza vaccination (SLIV) program implemented in over 102 elementary schools in Oakland, California.MethodsWe conducted a retrospective cohort study among Kaiser Permanente Northern California (KPNC) members of all ages residing in either the intervention or a multivariate-matched comparison site from September 2011 - August 2017. Outcomes included medically attended acute respiratory illness (MAARI), influenza hospitalization, and Oseltamivir prescriptions. We estimated difference-in-differences (DIDs) in 2014-15, 2015-16, and 2016-17 using generalized linear models and adjusted for race, ethnicity, age, sex, health plan, and language.ResultsPre-intervention member characteristics were similar between sites. The proportion of KPNC members vaccinated for influenza by KPNC or the SLIV program was 8-11% higher in the intervention site than the comparison site during the intervention period. Among school-aged children, SLIV was associated with lower Oseltamivir prescriptions per 1,000 (DIDs: -3.5 (95% CI -5.5, -1.5) in 2015-16; -4.0 (95% CI -6.5, -1.6) in 2016-17) but not with other outcomes. SLIV was associated with lower MAARI per 1,000 in adults 65 + years (2014-15: -13.2, 95% CI -23.2, -3.2; 2015-16: -21.5, 95% CI -31.1, -11.9; 2016-17: -13.0, 95% CI -23.2, -2.9). There were few significant associations with other outcomes among adults.ConclusionsA city-wide SLIV intervention was associated with higher influenza vaccination coverage, lower Oseltamivir prescriptions in school-aged children, and lower MAARI among people over 65 years, suggesting possible indirect effects of SLIV among older adults.
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- 2021
12. Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application
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Kennedy, Chris J., Bacon, Geoff, Sahn, Alexander, and von Vacano, Claudia
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,Computer Science - Social and Information Networks ,I.2.7 - Abstract
We propose a general method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT). We decompose the target construct, hate speech in our case, into multiple constituent components that are labeled as ordinal survey items. Those survey responses are transformed via IRT into a debiased, continuous outcome measure. Our method estimates the survey interpretation bias of the human labelers and eliminates that influence on the generated continuous measure. We further estimate the response quality of each labeler using faceted IRT, allowing responses from low-quality labelers to be removed. Our faceted Rasch scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction on new data. The ratings on the theorized components of the target outcome are used as supervised, ordinal variables for the neural networks' internal concept learning. We test the use of an activation function (ordinal softmax) and loss function (ordinal cross-entropy) designed to exploit the structure of ordinal outcome variables. Our multitask architecture leads to a new form of model interpretation because each continuous prediction can be directly explained by the constituent components in the penultimate layer. We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers to measure a continuous spectrum from hate speech to counterspeech. We evaluate Universal Sentence Encoders, BERT, and RoBERTa as language representation models for the comment text, and compare our predictive accuracy to Google Jigsaw's Perspective API models, showing significant improvement over this standard benchmark., Comment: 35 pages, 10 figures
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- 2020
13. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium
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Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.
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- 2023
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14. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
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Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame
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- 2023
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15. Evaluation of a city-wide school-located influenza vaccination program in Oakland, California, with respect to vaccination coverage, school absences, and laboratory-confirmed influenza: A matched cohort study.
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Benjamin-Chung, Jade, Arnold, Benjamin F, Kennedy, Chris J, Mishra, Kunal, Pokpongkiat, Nolan, Nguyen, Anna, Jilek, Wendy, Holbrook, Kate, Pan, Erica, Kirley, Pam D, Libby, Tanya, Hubbard, Alan E, Reingold, Arthur, and Colford, John M
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Humans ,Influenza Vaccines ,Vaccination ,Cohort Studies ,Cross-Sectional Studies ,Absenteeism ,Schools ,Students ,Adolescent ,Child ,Child ,Preschool ,Urban Population ,School Health Services ,California ,Female ,Male ,Influenza ,Human ,Vaccination Coverage ,General & Internal Medicine ,Medical and Health Sciences - Abstract
BACKGROUND:It is estimated that vaccinating 50%-70% of school-aged children for influenza can produce population-wide indirect effects. We evaluated a city-wide school-located influenza vaccination (SLIV) intervention that aimed to increase influenza vaccination coverage. The intervention was implemented in ≥95 preschools and elementary schools in northern California from 2014 to 2018. Using a matched cohort design, we estimated intervention impacts on student influenza vaccination coverage, school absenteeism, and community-wide indirect effects on laboratory-confirmed influenza hospitalizations. METHODS AND FINDINGS:We used a multivariate matching algorithm to identify a nearby comparison school district with pre-intervention characteristics similar to those of the intervention school district and matched schools in each district. To measure student influenza vaccination, we conducted cross-sectional surveys of student caregivers in 22 school pairs (2017 survey, N = 6,070; 2018 survey, N = 6,507). We estimated the incidence of laboratory-confirmed influenza hospitalization from 2011 to 2018 using surveillance data from school district zip codes. We analyzed student absenteeism data from 2011 to 2018 from each district (N = 42,487,816 student-days). To account for pre-intervention differences between districts, we estimated difference-in-differences (DID) in influenza hospitalization incidence and absenteeism rates using generalized linear and log-linear models with a population offset for incidence outcomes. Prior to the SLIV intervention, the median household income was $51,849 in the intervention site and $61,596 in the comparison site. The population in each site was predominately white (41% in the intervention site, 48% in the comparison site) and/or of Hispanic or Latino ethnicity (26% in the intervention site, 33% in the comparison site). The number of students vaccinated by the SLIV intervention ranged from 7,502 to 10,106 (22%-28% of eligible students) each year. During the intervention, influenza vaccination coverage among elementary students was 53%-66% in the comparison district. Coverage was similar between the intervention and comparison districts in influenza seasons 2014-2015 and 2015-2016 and was significantly higher in the intervention site in seasons 2016-2017 (7%; 95% CI 4, 11; p < 0.001) and 2017-2018 (11%; 95% CI 7, 15; p < 0.001). During seasons when vaccination coverage was higher among intervention schools and the vaccine was moderately effective, there was evidence of statistically significant indirect effects: The DID in the incidence of influenza hospitalization per 100,000 in the intervention versus comparison site was -17 (95% CI -30, -4; p = 0.008) in 2016-2017 and -37 (95% CI -54, -19; p < 0.001) in 2017-2018 among non-elementary-school-aged individuals and -73 (95% CI -147, 1; p = 0.054) in 2016-2017 and -160 (95% CI -267, -53; p = 0.004) in 2017-2018 among adults 65 years or older. The DID in illness-related school absences per 100 school days during the influenza season was -0.63 (95% CI -1.14, -0.13; p = 0.014) in 2016-2017 and -0.80 (95% CI -1.28, -0.31; p = 0.001) in 2017-2018. Limitations of this study include the use of an observational design, which may be subject to unmeasured confounding, and caregiver-reported vaccination status, which is subject to poor recall and low response rates. CONCLUSIONS:A city-wide SLIV intervention in a large, diverse urban population was associated with a decrease in the incidence of laboratory-confirmed influenza hospitalization in all age groups and a decrease in illness-specific school absence rate among students in 2016-2017 and 2017-2018, seasons when the vaccine was moderately effective, suggesting that the intervention produced indirect effects. Our findings suggest that in populations with moderately high background levels of influenza vaccination coverage, SLIV programs are associated with further increases in coverage and reduced influenza across the community.
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- 2020
16. #Vape: Measuring E-Cigarette Influence on Instagram With Deep Learning and Text Analysis
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Vassey, Julia, Metayer, Catherine, Kennedy, Chris J, and Whitehead, Todd P
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Communication and Media Studies ,Linguistics ,Human Society ,Language ,Communication and Culture ,Sociology ,Good Health and Well Being ,vape ,vaping ,e-cigarettes ,social media ,instagram ,deep learning ,images ,Communication and media studies - Abstract
E-cigarette use is increasing dramatically among adolescents as social media marketing portrays "vaping" products as healthier alternatives to conventional cigarettes. In September 2018, the Food and Drug Administration (FDA) launched an anti-vaping campaign, in U.S. high schools, on social media and other platforms, emphasizing "The Real Cost" of e-cigarettes. Using a novel deep learning approach, we assessed changes in vaping-related content on Instagram from 2017 to 2019 and drew an inference about the initial impact of the FDA's Real Cost campaign on Instagram. We collected 245,894 Instagram posts that used vaping-related hashtags (e.g., #vape, #ejuice) in four samples from 2017 to 2019. We compared the "like" count from these posts before and after the FDA campaign. We used deep learning image classification to analyze 49,655 Instagram image posts, separating images of men, women, and vaping devices. We also conducted text analysis and topic modeling to detect the common words and themes in the posted captions. Since September 2018, the FDA-sponsored hashtag #TheRealCost has been used about 50 times per month on Instagram, whereas vaping-related hashtags we tracked were used up to 10,000 times more often. Comparing the pre-intervention (2017, 2018) and post-intervention (2019) samples of vaping-related Instagram posts, we found a three-fold increase in the median "like" count (10 vs. 28) and a 6-fold increase in the proportion of posts with more than 100 likes (2 vs. 15%). Over 70% of Instagram vaping images featured e-juices and devices, with a growing number of images depicting a "pod," the type of discrete vaping device that delivers high concentration of nicotine and is favored by novice e-cigarette users. In addition, the Instagram analytics data shared by the vaping influencers we interviewed showed underage Instagram users among their followers.
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- 2020
17. Impact of a city-wide school-located influenza vaccination program over four years on vaccination coverage, school absences, and laboratory-confirmed influenza: a prospective matched cohort study
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Benjamin-Chung, Jade, Arnold, Benjamin F, Kennedy, Chris J, Mishra, Kunal, Pokpongkiat, Nolan, Nguyen, Anna, Jilek, Wendy, Holbrook, Kate, Pan, Erica, Kirley, Pam D, Libby, Tanya, Hubbard, Alan E, Reingold, Arthur, and Colford, John M
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Prevention ,Influenza ,Clinical Research ,Immunization ,Vaccine Related ,Pneumonia & Influenza ,Infectious Diseases ,Prevention of disease and conditions ,and promotion of well-being ,3.4 Vaccines ,Infection ,Good Health and Well Being - Abstract
AbstractBackgroundIt is estimated that vaccinating 50-70% of school-aged children for influenza can produce population-wide indirect effects. We evaluated a city-wide, school-located influenza vaccination (SLIV) intervention that aimed to increase influenza vaccination coverage. The intervention was implemented in over 95 pre-schools and elementary schools in northern California from 2014 to 2018. Using a matched prospective cohort design, we estimated intervention impacts on student influenza vaccination coverage, school absenteeism, and community-wide indirect effects on laboratory-confirmed influenza hospitalizations.Methods and FindingsWe used a multivariate matching algorithm to identify a nearby comparison school district with similar pre-intervention characteristics and matched schools in each district. To measure student influenza vaccination, we conducted cross-sectional surveys of student caregivers in 22 school pairs (2016 survey N = 6,070; 2017 survey N = 6,507). We estimated the incidence of laboratory-confirmed influenza hospitalization from 2011-2018 using surveillance data from school district zip codes. We analyzed student absenteeism data from 2011-2018 from each district (N = 42,487,816 student-days). To account for pre-intervention differences between districts, we estimated difference-in-differences (DID) in influenza hospitalization incidence and absenteeism rates using generalized linear and log-linear models with a population offset for incidence outcomes.The number of students vaccinated by the SLIV intervention ranged from 7,502 to 10,106 (22-28% of eligible students) each year. During the intervention, influenza vaccination coverage among elementary students was 53-66% in the comparison district. Coverage was similar between the intervention and comparison districts in 2014-15 and 2015-16 and was significantly higher in the intervention site in 2016-17 (7% 95% CI 4, 11) and 2017-18 (11% 95% CI 7, 15). During seasons when vaccination coverage was higher among intervention schools and the vaccine was moderately effective, there was evidence of statistically significant indirect effects: adjusting for pre-intervention differences between districts, the reduction in influenza hospitalizations in the intervention site was 76 (95% CI 20, 133) in 2016-17 and 165 (95% CI 86, 243) in 2017-18 among non-elementary school aged individuals and 327 (5, 659) in 2016-17 and 715 (236, 1195) in 2017-18 among adults 65 years or older. The reduction in illness-related school absences during influenza season was 3,538 (95% CI 709, 6,366) in 2016-17 and 8,249 (95% CI 3,213, 13,285) in 2017-18. Limitations of this study include the use of an observational design, which may be subject to unmeasured confounding, and caregiver-reported vaccination status, which is subject to poor recall and low response rates.ConclusionA city-wide SLIV intervention in a large, diverse urban population decreased the incidence of laboratory-confirmed influenza hospitalization in all age groups and decreased illness-specific school absence rates among students during seasons when the vaccine was moderately effective, suggesting that the intervention produced indirect effects. Our findings suggest that in populations with moderately high background levels of influenza vaccination coverage, SLIV programs can further increase coverage and reduce influenza across communities.
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- 2019
18. Patterns and correlates of mental healthcare utilization during the COVID-19 pandemic among individuals with pre-existing mental disorder
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Lee, Hyunjoon, primary, Kennedy, Chris J., additional, Tu, Allison, additional, Restivo, Juliana, additional, Liu, Cindy H., additional, Naslund, John A., additional, Patel, Vikram, additional, Choi, Karmel W., additional, and Smoller, Jordan W., additional
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- 2024
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19. Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems
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Deo, Anthony J, primary, Castro, Victor M, additional, Baker, Ashley, additional, Carroll, Devon, additional, Gonzalez-Heydrich, Joseph, additional, Henderson, David C, additional, Holt, Daphne J, additional, Hook, Kimberly, additional, Karmacharya, Rakesh, additional, Roffman, Joshua L, additional, Madsen, Emily M, additional, Song, Eugene, additional, Adams, William G, additional, Camacho, Luisa, additional, Gasman, Sarah, additional, Gibbs, Jada S, additional, Fortgang, Rebecca G, additional, Kennedy, Chris J, additional, Lozinski, Galina, additional, Perez, Daisy C, additional, Wilson, Marina, additional, Reis, Ben Y, additional, and Smoller, Jordan W, additional
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- 2024
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20. Frequency of social media use and exposure to tobacco or nicotine-related content in association with E-cigarette use among youth: A cross-sectional and longitudinal survey analysis
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Vassey, Julia, Galimov, Arthur, Kennedy, Chris J., Vogel, Erin A., and Unger, Jennifer B.
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- 2022
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21. Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines
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Kennedy, Chris J., Marwaha, Jayson S., Beaulieu-Jones, Brendin R., Scalise, P. Nina, Robinson, Kortney A., Booth, Brandon, Fleishman, Aaron, Nathanson, Larry A., and Brat, Gabriel A.
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- 2022
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22. Who doesn’t fit? A multi-institutional study using machine learning to uncover the limits of opioid prescribing guidelines
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Yu, Justin K., Marwaha, Jayson S., Kennedy, Chris J., Robinson, Kortney A., Fleishman, Aaron, Beaulieu-Jones, Brendin R., Bleicher, Josh, Huang, Lyen C., Szolovits, Peter, and Brat, Gabriel A.
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- 2022
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23. Predicting suicide attempts among U.S. Army soldiers after leaving active duty using information available before leaving active duty: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS)
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Stanley, Ian H., Chu, Carol, Gildea, Sarah M., Hwang, Irving H., King, Andrew J., Kennedy, Chris J., Luedtke, Alex, Marx, Brian P., O’Brien, Robert, Petukhova, Maria V., Sampson, Nancy A., Vogt, Dawne, Stein, Murray B., Ursano, Robert J., and Kessler, Ronald C.
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- 2022
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24. Obstetric comorbidity scores and disparities in severe maternal morbidity across marginalized groups
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Leonard, Stephanie A., Main, Elliott K., Lyell, Deirdre J., Carmichael, Suzan L., Kennedy, Chris J., Johnson, Christina, and Mujahid, Mahasin S.
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- 2022
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25. Can polygenic scores enhance the predictive performance of clinical risk models for suicide attempts in a psychiatric emergency room setting? (Preprint)
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Lee, Younga Heather, primary, Zhang, Yingzhe, additional, Kennedy, Chris J, additional, Mallard, Travis T, additional, Liu, Zhaowen, additional, Vu, Phuong Linh, additional, Feng, Yen-Chen Anne, additional, Ge, Tian, additional, Petukhova, Maria V, additional, Kessler, Ronald C, additional, Nock, Matthew K, additional, and Smoller, Jordan W, additional
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- 2024
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26. Validation of an ICD-code-based case definition for psychotic illness across three health systems
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Deo, Anthony J., primary, Castro, Victor M., additional, Baker, Ashley, additional, Carroll, Devon, additional, Gonzalez-Heydrich, Joseph, additional, Henderson, David C., additional, Holt, Daphne J., additional, Hook, Kimberly, additional, Karmacharya, Rakesh, additional, Roffman, Joshua L., additional, Madsen, Emily M., additional, Song, Eugene, additional, Adams, William G., additional, Camacho, Luisa, additional, Gasman, Sarah, additional, Gibbs, Jada S., additional, Fortgang, Rebecca G., additional, Kennedy, Chris J., additional, Lozinski, Galina, additional, Perez, Daisy C., additional, Wilson, Marina, additional, Reis, Ben Y., additional, and Smoller, Jordan W., additional
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- 2024
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27. Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning
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Goodman, Emmett D., primary, Patel, Krishna K., additional, Zhang, Yilun, additional, Locke, William, additional, Kennedy, Chris J., additional, Mehrotra, Rohan, additional, Ren, Stephen, additional, Guan, Melody, additional, Zohar, Orr, additional, Downing, Maren, additional, Chen, Hao Wei, additional, Clark, Jevin Z., additional, Berrigan, Margaret T., additional, Brat, Gabriel A., additional, and Yeung-Levy, Serena, additional
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- 2024
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28. When does natural science uncertainty translate into economic uncertainty?
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McDermott, Shana M., Finnoff, David C., Shogren, Jason F., and Kennedy, Chris J.
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- 2021
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29. Factors Associated with Hepatitis B Knowledge Among Vietnamese Americans: A Population-Based Survey
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Chu, Janet N, Le, Phuoc V, Kennedy, Chris J, McPhee, Stephen J, Wong, Ching, Stewart, Susan L, and Nguyen, Tung T
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Epidemiology ,Public Health ,Health Sciences ,Liver Disease ,Clinical Research ,Hepatitis - B ,Digestive Diseases ,Infectious Diseases ,Hepatitis ,Infection ,Good Health and Well Being ,Acculturation ,Adolescent ,Adult ,Age Factors ,Asian ,California ,Emigrants and Immigrants ,Female ,Health Knowledge ,Attitudes ,Practice ,Hepatitis B ,Humans ,Language ,Male ,Middle Aged ,Sex Factors ,Socioeconomic Factors ,Vietnam ,Young Adult ,Asian American ,Vietnamese American ,Liver disease ,Health disparities ,Public Health and Health Services ,Public health ,Sociology - Abstract
Vietnamese Americans have high rates of hepatitis B virus (HBV) infection but low rates of knowledge and screening. A population-based survey conducted in 2011 of Vietnamese Americans in two geographic areas (n = 1666) was analyzed. The outcome variables were having heard of HBV and a score summarizing knowledge of HBV transmission. Most respondents (86.0%) had heard of HBV. Correct knowledge of transmission ranged from 59.5% for sex, 68.1% for sharing toothbrushes, 78.6% for during birth, and 85.0% for sharing needles. In multivariable analyses, factors associated with having heard of HBV and higher knowledge included Northern California residence, longer U.S. residence, higher education, family history of HBV, and discussing HBV with family/friends. Higher income was associated with having heard of HBV. English fluency and being U.S.-born were associated with higher knowledge. Interventions to increase knowledge of HBV transmission are needed to decrease this health disparity among Vietnamese Americans.
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- 2017
30. Correction: The stories about racism and health: the development of a framework for racism narratives in medical literature using a computational grounded theory approach
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Figueroa, C.A. (author), Manalo-Pedro, Erin (author), Pola, Swetha (author), Darwish, Sajia (author), Sachdeva, Pratik (author), Guerrero, Christian (author), von Vacano, Claudia (author), Jha, Maithili (author), De Maio, Fernando (author), Kennedy, Chris J. (author), Figueroa, C.A. (author), Manalo-Pedro, Erin (author), Pola, Swetha (author), Darwish, Sajia (author), Sachdeva, Pratik (author), Guerrero, Christian (author), von Vacano, Claudia (author), Jha, Maithili (author), De Maio, Fernando (author), and Kennedy, Chris J. (author)
- Abstract
After publication of this article [1], the authors reported that the disclaimer statement in the backmatter was missing and should have read ‘Disclaimer: The ideas in this article are those of the authors and do not necessarily represent policy of the American Medical Association.’ The original article [1] has been corrected. (International Journal for Equity in Health, (2023), 22, 1, (265), 10.1186/s12939-023-02077-0), Information and Communication Technology
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- 2024
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31. Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision.
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Vassey, Julia, Kennedy, Chris J, Chang, Ho-Chun Herbert, Smith, Ashley S, and Unger, Jennifer B
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COMPUTER vision , *SOCIAL media , *OBJECT recognition (Computer vision) , *ELECTRONIC cigarettes , *BRAND name products - Abstract
Introduction Instagram and TikTok, video-based social media platforms popular among adolescents, contain tobacco-related content despite the platforms' policies prohibiting substance-related posts. Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos. Aims and Methods We created a data set of 6999 Instagram images labeled for 8 object classes: mod or pod devices, e-juice containers, packaging boxes, nicotine warning labels, e-juice flavors, e-cigarette brand names, and smoke clouds. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model's performance on 20 Instagram and TikTok videos, and applied the model to 14 072 e-cigarette-related promotional TikTok videos (2019–2022; 10 276 485 frames). Results The model achieved the following mean average precision scores on the image test set: e-juice container: 0.89; pod device: 0.67; mod device: 0.54; packaging box: 0.84; nicotine warning label: 0.86; e-cigarette brand name: 0.71; e-juice flavor name: 0.89; and smoke cloud: 0.46. The prevalence of pod devices in promotional TikTok videos increased by 15% from 2019 to 2022. The prevalence of e-juices increased by 33% from 2021 to 2022. The prevalence of e-juice flavor names and e-cigarette brand names increased by about 100% from 2019 to 2022. Conclusions Deep learning-based object detection technology enables automated analysis of visual posts on social media. Our computer vision model can detect the presence of e-cigarettes products in images and videos, providing valuable surveillance data for tobacco regulatory science (TRS). Implications Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model's performance on 20 Instagram and TikTok videos featuring at least two e-cigarette objects, and applied the model to 14 072 e-cigarette-related promotional TikTok videos (2019–2022; 10 276 485 frames). The deep learning model can be used for automated, scalable surveillance of image- and video-based e-cigarette-related promotional content on social media, providing valuable data for TRS. Social media platforms could use computer vision to identify tobacco-related imagery and remove it promptly, which could reduce adolescents' exposure to tobacco content online. [ABSTRACT FROM AUTHOR]
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- 2024
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32. Farmer preferences for reforestation contracts in Brazil's Atlantic Forest
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Richards, Ryan C., Petrie, Ragan, Christ, Benjamin, Ditt, Eduardo, and Kennedy, Chris J.
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- 2020
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33. Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision
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Vassey, Julia, primary, Kennedy, Chris J, additional, Herbert Chang, Ho-Chun, additional, Smith, Ashley S, additional, and Unger, Jennifer B, additional
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- 2023
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34. Data-Adaptive Target Parameters
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Hubbard, Alan E., Kennedy, Chris J., van der Laan, Mark J., Bickel, Peter, Series Editor, Diggle, Peter, Series Editor, Fienberg, Stephen E., Series Editor, Gather, Ursula, Series Editor, Zeger, Scott, Series Editor, van der Laan, Mark J., and Rose, Sherri
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- 2018
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35. Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.
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Ross, Eric L., Bossarte, Robert M., Dobscha, Steven K., Gildea, Sarah M., Hwang, Irving, Kennedy, Chris J., Liu, Howard, Luedtke, Alex, Marx, Brian P., Nock, Matthew K., Petukhova, Maria V., Sampson, Nancy A., Zainal, Nur Hani, Sverdrup, Erik, Wager, Stefan, and Kessler, Ronald C.
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SUICIDAL behavior ,PEOPLE with mental illness ,SUICIDE risk factors ,PSYCHIATRIC treatment ,ATTEMPTED suicide ,PSYCHIATRIC hospital care - Abstract
Key Points: Question: Can development of an individualized treatment rule identify patients presenting to emergency departments/urgent care with suicidal ideation or suicide attempts who are likely to benefit from psychiatric hospitalization? Findings: A decision analytic model found that hospitalization was associated with reduced suicide attempt risk among patients who attempted suicide in the past day but not among others with suicidality. Accounting for heterogeneity, suicide attempt risk was found to increase with hospitalization in 24% of patients and decrease in 28%. Meaning: Results of this study suggest that implementing an individualized treatment rule could identify many additional patients who may benefit from or be harmed by hospitalization. Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, −0.1%; 95% CI, −0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from −6.9% to −9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations. This predictive analytics model develops a preliminary individualized treatment rule to estimate the association between psychiatric hospitalization and subsequent suicidal behaviors in patients at risk for suicide. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors
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Ross, Eric L., primary, Bossarte, Robert M., additional, Dobscha, Steven K., additional, Gildea, Sarah M., additional, Hwang, Irving, additional, Kennedy, Chris J., additional, Liu, Howard, additional, Luedtke, Alex, additional, Marx, Brian P., additional, Nock, Matthew K., additional, Petukhova, Maria V., additional, Sampson, Nancy A., additional, Zainal, Nur Hani, additional, Sverdrup, Erik, additional, Wager, Stefan, additional, and Kessler, Ronald C., additional
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- 2023
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37. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium
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Sperotto, Francesca, primary, Gutiérrez-Sacristán, Alba, additional, Makwana, Simran, additional, Li, Xiudi, additional, Rofeberg, Valerie N., additional, Cai, Tianxi, additional, Bourgeois, Florence T., additional, Omenn, Gilbert S., additional, Hanauer, David A., additional, Sáez, Carlos, additional, Bonzel, Clara-Lea, additional, Bucholz, Emily, additional, Dionne, Audrey, additional, Elias, Matthew D., additional, García-Barrio, Noelia, additional, González, Tomás González, additional, Issitt, Richard W., additional, Kernan, Kate F., additional, Laird-Gion, Jessica, additional, Maidlow, Sarah E., additional, Mandl, Kenneth D., additional, Ahooyi, Taha Mohseni, additional, Moraleda, Cinta, additional, Morris, Michele, additional, Moshal, Karyn L., additional, Pedrera-Jiménez, Miguel, additional, Shah, Mohsin A., additional, South, Andrew M., additional, Spiridou, Anastasia, additional, Taylor, Deanne M., additional, Verdy, Guillaume, additional, Visweswaran, Shyam, additional, Wang, Xuan, additional, Xia, Zongqi, additional, Zachariasse, Joany M., additional, Newburger, Jane W., additional, Avillach, Paul, additional, Aaron, James R., additional, Adam, Atif, additional, Agapito, Giuseppe, additional, Albayrak, Adem, additional, Albi, Giuseppe, additional, Alessiani, Mario, additional, Alloni, Anna, additional, Amendola, Danilo F., additional, Angoulvant, François, additional, Anthony, Li LLJ., additional, Aronow, Bruce J., additional, Ashraf, Fatima, additional, Atz, Andrew, additional, Panickan, Vidul Ayakulangara, additional, Azevedo, Paula S., additional, Badenes, Rafael, additional, Balshi, James, additional, Batugo, Ashley, additional, Beaulieu-Jones, Brendin R., additional, Beaulieu-Jones, Brett K., additional, Bell, Douglas S., additional, Bellasi, Antonio, additional, Bellazzi, Riccardo, additional, Benoit, Vincent, additional, Beraghi, Michele, additional, Bernal-Sobrino, José Luis, additional, Bernaux, Mélodie, additional, Bey, Romain, additional, Bhatnagar, Surbhi, additional, Blanco-Martínez, Alvar, additional, Boeker, Martin, additional, Booth, John, additional, Bosari, Silvano, additional, Bradford, Robert L., additional, Brat, Gabriel A., additional, Bréant, Stéphane, additional, Brown, Nicholas W., additional, Bruno, Raffaele, additional, Bryant, William A., additional, Bucalo, Mauro, additional, Burgun, Anita, additional, Cannataro, Mario, additional, Carmona, Aldo, additional, Cattelan, Anna Maria, additional, Caucheteux, Charlotte, additional, Champ, Julien, additional, Chen, Jin, additional, Chen, Krista Y., additional, Chiovato, Luca, additional, Chiudinelli, Lorenzo, additional, Cho, Kelly, additional, Cimino, James J., additional, Colicchio, Tiago K., additional, Cormont, Sylvie, additional, Cossin, Sébastien, additional, Craig, Jean B., additional, Cruz-Bermúdez, Juan Luis, additional, Cruz-Rojo, Jaime, additional, Dagliati, Arianna, additional, Daniar, Mohamad, additional, Daniel, Christel, additional, Das, Priyam, additional, Devkota, Batsal, additional, Duan, Rui, additional, Dubiel, Julien, additional, DuVall, Scott L., additional, Esteve, Loic, additional, Estiri, Hossein, additional, Fan, Shirley, additional, Follett, Robert W., additional, Ganslandt, Thomas, additional, Garmire, Lana X., additional, Gehlenborg, Nils, additional, Getzen, Emily J., additional, Geva, Alon, additional, Goh, Rachel SJ., additional, Gradinger, Tobias, additional, Gramfort, Alexandre, additional, Griffier, Romain, additional, Griffon, Nicolas, additional, Grisel, Olivier, additional, Guzzi, Pietro H., additional, Han, Larry, additional, Haverkamp, Christian, additional, Hazard, Derek Y., additional, He, Bing, additional, Henderson, Darren W., additional, Hilka, Martin, additional, Ho, Yuk-Lam, additional, Holmes, John H., additional, Honerlaw, Jacqueline P., additional, Hong, Chuan, additional, Huling, Kenneth M., additional, Hutch, Meghan R., additional, Jannot, Anne Sophie, additional, Jouhet, Vianney, additional, Kainth, Mundeep K., additional, Kate, Kernan F., additional, Kavuluru, Ramakanth, additional, Keller, Mark S., additional, Kennedy, Chris J., additional, Key, Daniel A., additional, Kirchoff, Katie, additional, Klann, Jeffrey G., additional, Kohane, Isaac S., additional, Krantz, Ian D., additional, Kraska, Detlef, additional, Krishnamurthy, Ashok K., additional, L'Yi, Sehi, additional, Leblanc, Judith, additional, Lemaitre, Guillaume, additional, Lenert, Leslie, additional, Leprovost, Damien, additional, Liu, Molei, additional, Will Loh, Ne Hooi, additional, Long, Qi, additional, Lozano-Zahonero, Sara, additional, Luo, Yuan, additional, Lynch, Kristine E., additional, Mahmood, Sadiqa, additional, Makoudjou, Adeline, additional, Malovini, Alberto, additional, Mao, Chengsheng, additional, Maram, Anupama, additional, Maripuri, Monika, additional, Martel, Patricia, additional, Martins, Marcelo R., additional, Marwaha, Jayson S., additional, Masino, Aaron J., additional, Mazzitelli, Maria, additional, Mazzotti, Diego R., additional, Mensch, Arthur, additional, Milano, Marianna, additional, Minicucci, Marcos F., additional, Moal, Bertrand, additional, Moore, Jason H., additional, Morris, Jeffrey S., additional, Mousavi, Sajad, additional, Mowery, Danielle L., additional, Murad, Douglas A., additional, Murphy, Shawn N., additional, Naughton, Thomas P., additional, Breda Neto, Carlos Tadeu, additional, Neuraz, Antoine, additional, Newburger, Jane, additional, Ngiam, Kee Yuan, additional, Njoroge, Wanjiku FM., additional, Norman, James B., additional, Obeid, Jihad, additional, Okoshi, Marina P., additional, Olson, Karen L., additional, Orlova, Nina, additional, Ostasiewski, Brian D., additional, Palmer, Nathan P., additional, Paris, Nicolas, additional, Patel, Lav P., additional, Pfaff, Ashley C., additional, Pfaff, Emily R., additional, Pillion, Danielle, additional, Pizzimenti, Sara, additional, Priya, Tanu, additional, Prokosch, Hans U., additional, Prudente, Robson A., additional, Prunotto, Andrea, additional, Quirós-González, Víctor, additional, Ramoni, Rachel B., additional, Raskin, Maryna, additional, Rieg, Siegbert, additional, Roig-Domínguez, Gustavo, additional, Rojo, Pablo, additional, Romero-Garcia, Nekane, additional, Rubio-Mayo, Paula, additional, Sacchi, Paolo, additional, Salamanca, Elisa, additional, Samayamuthu, Malarkodi Jebathilagam, additional, Sanchez-Pinto, L. Nelson, additional, Sandrin, Arnaud, additional, Santhanam, Nandhini, additional, Santos, Janaina C.C., additional, Sanz Vidorreta, Fernando J., additional, Savino, Maria, additional, Schriver, Emily R., additional, Schubert, Petra, additional, Schuettler, Juergen, additional, Scudeller, Luigia, additional, Sebire, Neil J., additional, Serrano-Balazote, Pablo, additional, Serre, Patricia, additional, Serret-Larmande, Arnaud, additional, Hossein Abad, Zahra Shakeri, additional, Silvio, Domenick, additional, Sliz, Piotr, additional, Son, Jiyeon, additional, Sonday, Charles, additional, Sperotto, Francesca, additional, Strasser, Zachary H., additional, Tan, Amelia LM., additional, Tan, Bryce W.Q., additional, Tan, Byorn W.L., additional, Tanni, Suzana E., additional, Terriza-Torres, Ana I., additional, Tibollo, Valentina, additional, Tippmann, Patric, additional, Toh, Emma MS., additional, Torti, Carlo, additional, Trecarichi, Enrico M., additional, Vallejos, Andrew K., additional, Varoquaux, Gael, additional, Vella, Margaret E., additional, Vie, Jill-Jênn, additional, Vitacca, Michele, additional, Wagholikar, Kavishwar B., additional, Waitman, Lemuel R., additional, Wassermann, Demian, additional, Weber, Griffin M., additional, Wolkewitz, Martin, additional, Wong, Scott, additional, Xiong, Xin, additional, Ye, Ye, additional, Yehya, Nadir, additional, Yuan, William, additional, Zahner, Janet J., additional, Zambelli, Alberto, additional, Zhang, Harrison G., additional, Zöller, Daniela, additional, Zuccaro, Valentina, additional, and Zucco, Chiara, additional
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- 2023
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38. Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record
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Kennedy, Chris J., primary, Chiu, Catherine, additional, Chapman, Allyson Cook, additional, Gologorskaya, Oksana, additional, Farhan, Hassan, additional, Han, Mary, additional, Hodgson, MacGregor, additional, Lazzareschi, Daniel, additional, Ashana, Deepshikha, additional, Lee, Sei, additional, Smith, Alexander K., additional, Espejo, Edie, additional, Boscardin, John, additional, Pirracchio, Romain, additional, and Cobert, Julien, additional
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- 2023
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39. Independence by Competition: Computational Social Science in the Age of Data Brokers (Preprint)
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Chang, Ho-Chun Herbert, primary, Vassey, Julia, additional, Kennedy, Chris J., additional, and Unger, Jennifer B., additional
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- 2023
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40. Comparing Rationale for Opioid Prescribing Decisions after Surgery with Subsequent Patient Consumption: A Survey of the Highest Quartile of Prescribers
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Beaulieu-Jones, Brendin R, primary, Marwaha, Jayson S, additional, Kennedy, Chris J, additional, Le, Danny, additional, Berrigan, Margaret T, additional, Nathanson, Larry A, additional, and Brat, Gabriel A, additional
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- 2023
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41. A Precision Treatment Model for Internet-Delivered Cognitive Behavioral Therapy for Anxiety and Depression Among University Students
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Benjet, Corina, primary, Zainal, Nur Hani, additional, Albor, Yesica, additional, Alvis-Barranco, Libia, additional, Carrasco-Tapias, Nayib, additional, Contreras-Ibáñez, Carlos C., additional, Cudris-Torres, Lorena, additional, de la Peña, Francisco R., additional, González, Noé, additional, Guerrero-López, José Benjamín, additional, Gutierrez-Garcia, Raúl A., additional, Jiménez-Peréz, Ana Lucía, additional, Medina-Mora, Maria Elena, additional, Patiño, Pamela, additional, Cuijpers, Pim, additional, Gildea, Sarah M., additional, Kazdin, Alan E., additional, Kennedy, Chris J., additional, Luedtke, Alex, additional, Sampson, Nancy A., additional, Petukhova, Maria V., additional, and Kessler, Ronald C., additional
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- 2023
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42. In Silico Performance Versus Real-World Utility of Surgical Prediction Models: What Does it Take to Change a Surgeon’s Mind?
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Wang, Joyce E, primary, Kennedy, Chris J, additional, Brat, Gabriel A, additional, and Marwaha, Jayson S, additional
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- 2023
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43. Innovations in machine learning: interval latent variables, causal exposure mixtures, and clinical predictive modeling
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Kennedy, Chris J
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Biostatistics ,Statistics ,Computer science ,causal inference ,clinical prediction model ,exposure mixtures ,hate speech ,machine learning ,Rasch measurement - Abstract
This study presents three projects that build on machine learning techniques to propose new tools for scientific discovery: 1) constructing and measuring latent variables at scale by combining faceted Rasch measurement with supervised deep learning, 2) translating the problem of exposure mixtures (i.e. projecting multiple treatment variables onto a continuous summary measure) into a data-adaptive parameter within the targeted learning causal inference framework, and proposing an estimation algorithm, and 3) combining multiple techniques to improve the predictive accuracy and interpretability of clinical prediction models developed within the context of electronic health records.The first project (Chapter 2) develops a general methodology to combine faceted Rasch measurement, a theoretically optimal form of item response theory, with supervised deep learning to construct and measure arbitrary interval variables. Rasch measurement theory is a method to create interval latent variables that are not directly observed, but can be approximated by collecting data on a set of components that are believed to contain information that indicates where an observation falls on that latent spectrum. The faceted version of Rasch modeling extends the method to rater-mediated assessments, which are essentially what most deep learning projects entail when they rely on human labelers to generate a training dataset. Bringing the formal tools of item response theory to machine learning offers a host of benefits, including reducing survey interpretation bias in the labeled data and upgrading the target variable from a dichotomous or ordinal structure to a continuous, interval variable, increasing precision. Perhaps most importantly, Rasch measurement theory provides a structure for gradual iteration based on the interplay between theorization and empirical testing, without which the field of machine learning has been forced to develop ad hoc solutions.I realized in the course of the project that item response theory lent itself to a natural integration with a deep learning-based estimator for applying the constructed variable to new data. While the deep learning model could be designed to predict the interval variable directly, as would be standard practice, an alternative architecture became visible: train the deep learning estimator to predict the individual scale components (items) from the labeling instrument, then apply the item response theory transform to those components in an offline fashion. Such an architecture leads to a new form of model explanation, because unlike standard neural models where the final dense layers are randomly initialized and generate their own internal latent variables that predict the final score, in our system those final latent variables were directly proposed via theorization and we had labeled data for which the neural optimization could provide supervised feedback. Predicting each item as separate outcomes in a single neural network entails a "multitask" architecture: the system simultaneously optimizes its predictive accuracy for each task, i.e. the predicted rating on each item. Multitask architectures are believed to offer efficiency gains because correlation between tasks allows information about one task (item rating) to also inform the model's prediction on other related tasks. And in an item response theory model the items would generally be highly correlated due to their coordinated measurement of an underlying latent variable. Predicting the rating on each item offers another twist: those item ratings are ordinal variables, so if we incorporate that scientific knowledge in our estimator we have the potential to gain efficiency and generate predictions that are consistent with that ordinal structure (i.e. predicted probabilities are unimodal across the possible ratings).The second project (Chapter 3) examines the problem of estimating exposure mixtures, which are collections of treatment variables for which we seek to examine joint effects on a given outcome variable (e.g. disease state). Taking inspiration from the parametric method of weighted quantile sum regression, we recast the problem of exposure mixture estimation as a data-adaptive statistical parameter within the targeted learning causal inference framework. That allowed the establishment of an estimation procedure to nonparametrically project the vector of treatment variables onto a continuous latent variable that maximizes the joint relationship with the outcome, One could then evaluate causal parameters, such as treatment-specific means, on a held-out validation set using the cross-validated targeted maximum likelihood estimation procedure (CV-TMLE). Our method builds on earlier work with Alan Hubbard and Mark van der Laan as part of the varimpact algorithm, which is a data-adaptive method for causal variable importance that examines a single variable at a time. Through our mixture work we realized that an exposure mixture is a form of variable set importance, allowing subgroups of treatment variables to be ranked based on their combined mixture's estimated impact on the outcome variable.The final project (Chapter 4), seeks to provide a guide to the development of high-quality clinical prediction models based on electronic health record data. Through the process of creating a risk prediction model for future heart attacks, we propose improved methods for many steps, including generalized low-rank models for missing data imputation, penalized histogramming to manage the cardinality of imputed covariates, nested SuperLearner ensembling for interpretable hyperparameter optimization, accumulated local effect plots for model explanation, and the index of prediction accuracy as a general performance metric combining discrimination and calibration.
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- 2020
44. Considering farmer land use decisions in efforts to ‘scale up’ Payments for Watershed Services
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Richards, Ryan C., Kennedy, Chris J., Lovejoy, Thomas E., and Brancalion, Pedro H.S.
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- 2017
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45. Internet-Delivered Cognitive Behavior Therapy Versus Treatment as Usual for Anxiety and Depression Among Latin American University Students: A Randomized Clinical Trial.
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Benjet, Corina, Albor, Yesica, Alvis-Barranco, Libia, Contreras-Ibáñez, Carlos C., Cuartas, Gina, Cudris-Torres, Lorena, González, Noé, Cortés-Morelos, Jacqueline, Gutierrez-Garcia, Raúl A., Medina-Mora, Maria Elena, Patiño, Pamela, Vargas-Contreras, Eunice, Cuijpers, Pim, Gildea, Sarah M., Kazdin, Alan E., Kennedy, Chris J., Luedtke, Alex, Sampson, Nancy A., Petukhova, Maria V., and Zainal, Nur Hani
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COGNITIVE therapy ,CLINICAL trials ,BEHAVIOR therapy ,MENTAL depression ,MENTAL illness ,DIMENSIONAL analysis - Abstract
Objective: Untreated mental disorders are important among low- and middle-income country (LMIC) university students in Latin America, where barriers to treatment are high. Scalable interventions are needed. This study compared transdiagnostic self-guided and guided internet-delivered cognitive behavioral therapy (i-CBT) with treatment as usual (TAU) for clinically significant anxiety and depression among undergraduates in Colombia and Mexico. Method: 1,319 anxious, as determined by the Generalized Anxiety Disorder–7 (GAD-7) = 10+ and/or depressed, as determined by the Patient Health Questionnaire–9 (PHQ-9) = 10+, undergraduates (mean [SD] age = 21.4 [3.2]); 78.7% female; 55.9% first-generation university student) from seven universities in Colombia and Mexico were randomized to culturally adapted versions of self-guided i-CBT (n = 439), guided i-CBT (n = 445), or treatment as usual (TAU; n = 435). All randomized participants were reassessed 3 months after randomization. The primary outcome was remission of both anxiety (GAD-7 = 0–4) and depression (PHQ-9 = 0–4). We hypothesized that remission would be higher with guided i-CBT than with the other interventions. Results: Intent-to-treat analysis found significantly higher adjusted (for university and loss to follow-up) remission rates (ARD) among participants randomized to guided i-CBT than either self-guided i-CBT (ARD = 13.1%, χ
1 2 = 10.4, p =.001) or TAU (ARD = 11.2%, χ1 2 = 8.4, p =.004), but no significant difference between self-guided i-CBT and TAU (ARD = −1.9%, χ1 2 = 0.2, p =.63). Per-protocol sensitivity analyses and analyses of dimensional outcomes yielded similar results. Conclusions: Significant reductions in anxiety and depression among LMIC university students could be achieved with guided i-CBT, although further research is needed to determine which students would most likely benefit from this intervention. What is the public health significance of this article?: Anxiety and depression are significant public health problems in LMIC universities. A culturally adapted transdiagnostic-guided i-CBT could help alleviate these problems as a low-threshold intervention component of a stepped-care treatment delivery model. [ABSTRACT FROM AUTHOR]- Published
- 2023
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46. A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).
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Kearns, Jaclyn C., Edwards, Emily R., Finley, Erin P., Geraci, Joseph C., Gildea, Sarah M., Goodman, Marianne, Hwang, Irving, Kennedy, Chris J., King, Andrew J., Luedtke, Alex, Marx, Brian P., Petukhova, Maria V., Sampson, Nancy A., Seim, Richard W., Stanley, Ian H., Stein, Murray B., Ursano, Robert J., and Kessler, Ronald C.
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SUICIDE risk factors ,SELF-evaluation ,MACHINE learning ,RISK assessment ,SUICIDAL behavior ,SEVERITY of illness index ,QUESTIONNAIRES ,DESCRIPTIVE statistics ,RESEARCH funding ,PSYCHOLOGY of military personnel ,PREDICTION models ,RECEIVER operating characteristic curves ,SENSITIVITY & specificity (Statistics) ,DATA analysis software ,PSYCHOLOGICAL resilience ,LONGITUDINAL method - Abstract
Background: Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions. Methods: We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample. Results: Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs. Conclusions: An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA. [ABSTRACT FROM AUTHOR]
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- 2023
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47. An Expanded Obstetric Comorbidity Scoring System for Predicting Severe Maternal Morbidity
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Leonard, Stephanie A., Kennedy, Chris J., Carmichael, Suzan L., Lyell, Deirdre J., and Main, Elliott K.
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- 2020
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48. Toward personalized care for insomnia in the US Army: development of a machine learning model to predict response to pharmacotherapy
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Gabbay, Frances H., primary, Wynn, Gary H., additional, Georg, Matthew W., additional, Gildea, Sarah M., additional, Kennedy, Chris J., additional, King, Andrew J., additional, Sampson, Nancy A., additional, Ursano, Robert J., additional, Stein, Murray B., additional, Wagner, James R., additional, Kessler, Ronald C., additional, and Capaldi, Vincent F., additional
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- 2023
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49. A Proposed Synthesis for Surgeon Intuition and Machine Learning: A Prospective Study Quantifying The Prognostic Value of Surgeon Intuition
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Marwaha, Jayson, primary, Beaulieu-Jones, Brendin Ryan, additional, Chen, Hao Wei Chen, additional, Kennedy, Chris J, additional, Yuan, William, additional, Cook, Charles Howard, additional, and Brat, Gabriel, additional
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- 2023
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50. Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans
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Bossarte, Robert M., primary, Ross, Eric L., additional, Liu, Howard, additional, Turner, Brett, additional, Bryant, Corey, additional, Zainal, Nur Hani, additional, Puac-Polanco, Victor, additional, Ziobrowski, Hannah N., additional, Cui, Ruifeng, additional, Cipriani, Andrea, additional, Furukawa, Toshiaki A., additional, Leung, Lucinda B., additional, Joormann, Jutta, additional, Nierenberg, Andrew A., additional, Oslin, David W., additional, Pigeon, Wilfred R., additional, Post, Edward P., additional, Zaslavsky, Alan M., additional, Zubizarreta, Jose R., additional, Luedtke, Alex, additional, Kennedy, Chris J., additional, and Kessler, Ronald C., additional
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- 2023
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