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132 results on '"Polubriaginof, Fernanda"'

1. Impact of Exercise on Susceptibility and Severity of COVID-19 in Patients with Cancer: A Retrospective Study.

Author

Bliss, Joshua W, Lavery, Jessica A, Underwood, Whitney P, Chun, Su S, Fickera, Gina A, Lee, Catherine P, Corcoran, Stacie, Maloy, Molly A, Polubriaginof, Fernanda C, Kelly, Daniel W, Scott, Jessica M, Boutros, Paul C, Moskowitz, Chaya S, and Jones, Lee W

Subjects
Lung, Cancer, Clinical Research, Prevention, Good Health and Well Being, COVID-19, Exercise, Female, Humans, Male, Middle Aged, Neoplasms, Retrospective Studies, SARS-CoV-2, Surveys and Questionnaires, Medical and Health Sciences, Epidemiology
Abstract

BackgroundModifiable lifestyle-related factors heighten the risk and severity of coronavirus disease 2019 (COVID-19) in patients with cancer. Whether exercise lowers susceptibility or severity is not known.MethodsWe identified 944 cancer patients from Memorial Sloan Kettering Cancer Center (mean age: 64; 85% female; 78% White) completing an exercise survey before receiving a confirmed positive or negative SARS-CoV-2 test. Exercise was defined as reporting moderate-intensity ≥5 days per week, ≥30 minutes/session or strenuous-intensity ≥3 days per week, ≥20 minutes/session. Multivariable logistic regression was used to determine the relationship between exercise and COVID-19 susceptibility and severity (i.e., composite of hospital admission or death events) with adjustment for clinical-epidemiologic covariates.ResultsTwenty-four percent (230/944) of the overall cohort were diagnosed with COVID-19 and 35% (333/944) were exercisers. During a median follow-up of 10 months, 26% (156/611) of nonexercising patients were diagnosed with COVID-19 compared with 22% (74/333) of exercising patients. The adjusted OR for risk of COVID-19 was 0.65 [95% confidence interval (CI), 0.44-0.96, P = 0.03] for exercisers compared with nonexercisers. A total of 20% (47/230) of COVID-19 positive patients were hospitalized or died. No difference in the risk of severe COVID-19 as a function of exercise status was observed (P > 0.9).ConclusionsExercise may reduce the risk of COVID-19 infection in patients with a history of cancer, but not its severity.ImpactThis study provides the first data showing that exercise might lower the risk of COVID-19 in cancer patients, but further research is required.

Published
2022

3. Patient satisfaction with cancer care delivery through telemedicine at a comprehensive cancer center across three years during and after the COVID-19 pandemic.

Author

Doshi, Sahil D, primary, Khadem Charvadeh, Yasin, additional, Seier, Kenneth, additional, Bange, Erin Mary, additional, Daly, Robert Michael, additional, Polubriaginof, Fernanda C. G., additional, Buckley, Michael T., additional, Kuperman, Gilad, additional, Stetson, Peter D., additional, Schrag, Deb, additional, Morris, Michael J., additional, and Panageas, Katherine, additional

Published
2024
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4. Patient-reported experience with an immunotherapy telehealth platform.

Author

Daly, Robert Michael, primary, Bange, Erin Mary, additional, Doshi, Sahil D, additional, Li, Bob T., additional, Shah, Neil J., additional, Aggen, David H, additional, Kotecha, Ritesh R, additional, Eng, Juliana, additional, Ng, Kenneth K., additional, Polubriaginof, Fernanda C. G., additional, Kuperman, Gilad, additional, Lipitz-Snyderman, Allison, additional, Stetson, Peter D., additional, Schrag, Deb, additional, Panageas, Katherine, additional, and Morris, Michael J., additional

Published
2024
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8. Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies

Author

Shang, Ning, Khan, Atlas, Polubriaginof, Fernanda, Zanoni, Francesca, Mehl, Karla, Fasel, David, Drawz, Paul E., Carrol, Robert J., Denny, Joshua C., Hathcock, Matthew A., Arruda-Olson, Adelaide M., Peissig, Peggy L., Dart, Richard A., Brilliant, Murray H., Larson, Eric B., Carrell, David S., Pendergrass, Sarah, Verma, Shefali Setia, Ritchie, Marylyn D., Benoit, Barbara, Gainer, Vivian S., Karlson, Elizabeth W., Gordon, Adam S., Jarvik, Gail P., Stanaway, Ian B., Crosslin, David R., Mohan, Sumit, Ionita-Laza, Iuliana, Tatonetti, Nicholas P., Gharavi, Ali G., Hripcsak, George, Weng, Chunhua, and Kiryluk, Krzysztof

Published
2021
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13. Using electronic health record data to link families: an illustrative example using intergenerational patterns of obesity

Author

Krefman, Amy E, primary, Ghamsari, Farhad, additional, Turner, Daniel R, additional, Lu, Alice, additional, Borsje, Martin, additional, Wood, Colby Witherup, additional, Petito, Lucia C, additional, Polubriaginof, Fernanda C G, additional, Schneider, Daniel, additional, Ahmad, Faraz, additional, and Allen, Norrina B, additional

Published
2023
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15. Using machine learning to parse breast pathology reports

Author

Yala, Adam, Barzilay, Regina, Salama, Laura, Griffin, Molly, Sollender, Grace, Bardia, Aditya, Lehman, Constance, Buckley, Julliette M., Coopey, Suzanne B., Polubriaginof, Fernanda, Garber, Judy E., Smith, Barbara L., Gadd, Michele A., Specht, Michelle C., Gudewicz, Thomas M., Guidi, Anthony J., Taghian, Alphonse, and Hughes, Kevin S.

Published
2017
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18. Remote symptom monitoring after hospital discharge.

Author

Daly, Robert Michael, primary, Cracchiolo, Jennifer R., additional, Huang, Jennie, additional, Hannon, Michael, additional, Holland, Jessie C., additional, Begue, Aaron, additional, Mazzella, AnnMarie, additional, Salvaggio, Rori, additional, Polubriaginof, Fernanda C. G., additional, Kuperman, Gilad, additional, Roberts, Karen, additional, and Fahy, Rosanna Fiorarancio, additional

Published
2022
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21. Using machine learning to parse breast pathology reports

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Yala, Adam, Barzilay, Regina, Salama, Laura, Griffin, Molly, Sollender, Grace, Bardia, Aditya, Lehman, Constance, Buckley, Julliette M, Coopey, Suzanne B, Polubriaginof, Fernanda, Garber, Judy E, Smith, Barbara L, Gadd, Michele A, Specht, Michelle C, Gudewicz, Thomas M, Guidi, Anthony J, Taghian, Alphonse, Hughes, Kevin S, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Yala, Adam, Barzilay, Regina, Salama, Laura, Griffin, Molly, Sollender, Grace, Bardia, Aditya, Lehman, Constance, Buckley, Julliette M, Coopey, Suzanne B, Polubriaginof, Fernanda, Garber, Judy E, Smith, Barbara L, Gadd, Michele A, Specht, Michelle C, Gudewicz, Thomas M, Guidi, Anthony J, Taghian, Alphonse, and Hughes, Kevin S

Abstract

© 2016, Springer Science+Business Media New York. Purpose: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. Methods: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women’s Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. Results: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. Conclusions: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

Published
2021

24. Use of remote patient monitoring in the care of COVID-positive patients in oncology.

Author

Pugliese, Laura, primary, Garcia, Jericho, additional, Holland, Jessie C., additional, Majeed, Jibran, additional, Silverman, Max, additional, Moy, Morgan, additional, Kemeny, Elizabeth, additional, Reidy, Diane Lauren, additional, Robson, Mark E., additional, Connor, MaryAnn, additional, Stetson, Peter D., additional, and Polubriaginof, Fernanda C. G., additional

Published
2021
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27. Case study of the integration of electronic patient‐reported outcomes as standard of care in a head and neck oncology practice: Obstacles and opportunities

Author

Strachna, Olga, primary, Cohen, Marc A., additional, Allison, Monica M., additional, Pfister, David G., additional, Lee, Nancy Y., additional, Wong, Richard J., additional, McBride, Sean M., additional, Mohammed, Raia R., additional, Kemeny, Elizabeth, additional, Polubriaginof, Fernanda C. G., additional, Kassa, Alyse, additional, Hannon, Michael, additional, and Cracchiolo, Jennifer R., additional

Published
2020
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28. Rapid deployment of virtual mind-body interventions during the COVID-19 outbreak: feasibility, acceptability, and implications for future care

Author

Trevino, Kelly M., primary, Raghunathan, Nirupa, additional, Latte-Naor, Shelly, additional, Polubriaginof, Fernanda C. G., additional, Jensen, Claus, additional, Atkinson, Thomas M., additional, Emard, Nicholas, additional, Seluzicki, Christina M., additional, Ostroff, Jamie S., additional, and Mao, Jun J., additional

Published
2020
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30. 691: Do pregnant women have lower than expected measles antibody titers?

Author

Spiegelman, Jessica, primary, Handal-Orefice, Roxane, additional, da Graca Polubriaginof, Fernanda C., additional, Copeland, Timothy, additional, Loike, John, additional, Rosenfeld, David, additional, Glatt, Aaron E., additional, Bennett, Edward, additional, Simon, Michael A., additional, Dear, Moshe, additional, Nolan, Sheila, additional, Abberbock, Gary, additional, Friedman, Alexander M., additional, Wapner, Ronald J., additional, and Simpson, Lynn, additional

Published
2020
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34. Leveraging patient-provided data to improve understanding of disease risk

Author

Da Graca Polubriaginof, Fernanda Caroline

Subjects
FOS: Computer and information sciences, Bioinformatics, Medicine, Diseases--Risk factors, Medical sciences
Abstract

Patient-provided data are crucial to achieving the goal of precision medicine. These data, which include family medical history, race and ethnicity, and social and behavioral determinants of health, are essential for disease risk assessment. Despite the well-established importance of patient-provided data, there are many data quality challenges that affect how this information can be used for biomedical research. To determine how to best use patient-provided data to assess disease risk, the research reflected in this dissertation was divided into three overarching aims. In Aim 1, I focused on determining the quality of race and ethnicity, family history and smoking status in clinical databases. In Aim 2, I assessed the impact of various interventions on the quality of these data, including policy changes such as the implementation of the requirements imposed by the Meaningful Use program, and patient-facing tools for collecting and sharing information with patients. In addition to these evaluations, I also developed and evaluated a method “Relationship Inference from the Electronic Health Record” (RIFTEHR), that infers familial relationships from clinical datasets. In Aim 3, I used patient-provided data to assess disease risk both at a population level, by estimating disease heritability, and at an individual level, by identifying high-risk individuals eligible for additional screening for a common disease (diabetes mellitus) and a rare disease (celiac disease). My research uncovered several data quality concerns for patient-provided data in clinical databases. When assessing the impact of interventions on the quality of these data, I found that policy interventions led to more data collection, but not necessarily to better data quality. In contrast, patient-facing tools did increase the quality of the patient-provided data. In the absence of high-quality patient-provided data for family medical history, I developed and evaluated a method for inferring this information from large clinical databases. I demonstrated that electronic health record data can be used to infer familial relationships accurately. Moreover, I showed how the use of clinical data in conjunction with the inferred familial relationships could determine disease risk in two studies. In the first study, I successfully computed disease heritability estimates for 500 conditions, some of which had not been previously studied. In the second study, I identified that screening rates among family members that are considered to be at high-risk for disease development were low for both diabetes mellitus and celiac disease. In summary, the work represented in this dissertation contributes to the understanding of the quality of patient-provided data, how interventions affect the quality of these data, and how novel methods can be applied to troves of existing clinical data to generate new knowledge to support research and clinical care.

Published
2018
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37. Case study of the integration of electronic patient‐reported outcomes as standard of care in a head and neck oncology practice: Obstacles and opportunities.

Author

Strachna, Olga, Cohen, Marc A., Allison, Monica M., Pfister, David G., Lee, Nancy Y., Wong, Richard J., McBride, Sean M., Mohammed, Raia R., Kemeny, Elizabeth, Polubriaginof, Fernanda C. G., Kassa, Alyse, Hannon, Michael, and Cracchiolo, Jennifer R.

Subjects
PATIENT reported outcome measures, MEDICAL informatics, PATIENT participation, HEAD & neck cancer, DISEASE management
Abstract

Background: Patient‐reported outcomes (PROs) allow for the direct measurement of functional and psychosocial effects related to treatment. However, technological barriers, survey fatigue, and clinician adoption have hindered the meaningful integration of PROs into clinical care. The objective of the authors was to develop an electronic PROs (ePROs) program that meets a range of clinical needs across a head and neck multidisciplinary disease management team. Methods: The authors developed the ePROs module using literature review and stakeholder input in collaboration with health informatics. They designed an ePROs platform that was integrated as the standard of care for personalized survey delivery by diagnosis across the disease management team. Tableau software was used to create dashboards for data visualization and monitoring at the clinical enterprise, disease subsite, and patient levels. All patients who were treated for head and neck cancer were eligible for ePROs assessment as part of the standard of care. A descriptive analysis of ePROs program implementation is presented herein. Results: The Head and Neck Service at Memorial Sloan Kettering Cancer Center has integrated ePROs into clinical care. Surveys are delivered via the patient portal at the time of diagnosis and longitudinally through care. From August 1, 2018, to February 1, 2020, a total of 4154 patients completed ePROs surveys. The average patient participation rate was 69%, with a median time for completion of 5 minutes. Conclusions: Integration of the head and neck ePROs program as part of clinical care is feasible and could be used to assess value and counsel patients in the future. Continued qualitative assessments of stakeholders and workflow will refine content and enhance the health informatics platform. Lay Summary: Patients with head and neck cancer experience significant changes in their quality of life after treatment.Measuring and integrating patient‐reported outcomes as a part of clinical care have been challenging given the multimodal treatment options, vast subsites, and unique domains affected.The authors present a case study of the successful integration of electronic patient‐reported outcomes into a high‐volume head and neck cancer practice. Integration of an electronic patient‐reported outcomes (ePROs) program into a head and neck oncology practice is feasible and will allow for the measurement of outcomes. This program can be used as a model for the future development of ePROs programs across oncology practices. [ABSTRACT FROM AUTHOR]

Published
2021
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38. Validation of Case Identification for Alopecia Areata Using International Classification of Diseases Coding.

Author

Lavian, Jonathan, Jiayang Li, Sara, Eunice Yoojin Lee, Bordone, Lindsey A., Polubriaginof, Fernanda C. G., Christiano, Angela M., and Mostaghimi, Arash

Subjects
NOSOLOGY, ALOPECIA areata, WOMEN'S hospitals, ACADEMIC medical centers, SEARCH algorithms, MEDICAL records
Abstract

Background: Search algorithms used to identify patients with alopecia areata (AA) need to be validated prior to use in large databases. Objectives: The aim of the study is to assess whether patients with an International Statistical Classification of Diseases and Related Health Problems (ICD) 9 or 10 code for AA have a true diagnosis of AA. Materials and Methods: A multicenter retrospective review was performed at Columbia University Irving Medical Center, Brigham and Women's Hospital, and Massachusetts General Hospital to determine whether patients with an ICD 9 codes (704.01 -AA) or ICD 10 codes (L63.0 -Alopecia Totalis, L63.1 -Alopecia Universalis, L63.2 -Ophiasis, L63.8 -other AA, and L63.9 -AA, unspecified) for AA met diagnostic criteria for the disease. Results: Of 880 charts, 97.5% had physical examination findings consistent with AA, and 90% had an unequivocal diagnosis. AA was diagnosed by a dermatologist in 87% of the charts. The positive predictive value (PPV) of the ICD 9 code 704.01 was 97% (248/255). The PPV for the ICD 10 codes were 64% (75/118) for L63.0, 86% (130/151) for L63.1, 50% (1/2) for L63.2, 91% (81/89) for L63.8, and 93% (247/265) for L63.9. Overall, 89% (782/880) of patients with an ICD code for AA were deemed to have a true diagnosis of AA. Conclusions: Patients whose medical records contain an AA-associated ICD code have a high probability of having the condition. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Engaging hospital patients in the medication reconciliation process using tablet computers

Author

Prey, Jennifer E, primary, Polubriaginof, Fernanda, additional, Grossman, Lisa V, additional, Masterson Creber, Ruth, additional, Tsapepas, Demetra, additional, Perotte, Rimma, additional, Qian, Min, additional, Restaino, Susan, additional, Bakken, Suzanne, additional, Hripcsak, George, additional, Efird, Leigh, additional, Underwood, Joseph, additional, and Vawdrey, David K, additional

Published
2018
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41. Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Author

Polubriaginof, Fernanda C.G., primary, Vanguri, Rami, additional, Quinnies, Kayla, additional, Belbin, Gillian M., additional, Yahi, Alexandre, additional, Salmasian, Hojjat, additional, Lorberbaum, Tal, additional, Nwankwo, Victor, additional, Li, Li, additional, Shervey, Mark M., additional, Glowe, Patricia, additional, Ionita-Laza, Iuliana, additional, Simmerling, Mary, additional, Hripcsak, George, additional, Bakken, Suzanne, additional, Goldstein, David, additional, Kiryluk, Krzysztof, additional, Kenny, Eimear E., additional, Dudley, Joel, additional, Vawdrey, David K., additional, and Tatonetti, Nicholas P., additional

Published
2018
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43. Estimate of Disease Heritability Using 7.4 million Familial Relationships Inferred from Electronic Health Records

Author

Polubriaginof, Fernanda, primary, Vanguri, Rami, additional, Quinnies, Kayla, additional, Belbin, Gillian M., additional, Yahi, Alexandre, additional, Salmasian, Hojjat, additional, Lorberbaum, Tal, additional, Nwankwo, Victor, additional, Li, Li, additional, Shervey, Mark, additional, Glowe, Patricia, additional, Ionita-Laza, Iuliana, additional, Simmerling, Mary, additional, Hripcsak, George, additional, Bakken, Suzanne, additional, Goldstein, David, additional, Kiryluk, Krzysztof, additional, Kenny, Eimear E., additional, Dudley, Joel, additional, Vawdrey, David K., additional, and Tatonetti, Nicholas P., additional

Published
2018
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45. Exploration of Temporal ICD Coding Bias Related to Acute Diabetic Conditions

Author

McKillop, Mollie, Polubriaginof, Fernanda, and Weng, Chunhua

Subjects
Blood Glucose, Bias, International Classification of Diseases, Diabetes Mellitus, Humans, Hypoglycemic Agents, Articles
Abstract

Electronic Health Records (EHRs) hold great promise for secondary data reuse but have been reported to contain severe biases. The temporal characteristics of coding biases remain unclear. This study used a survival analysis approach to reveal temporal bias trends for coding acute diabetic conditions among 268 diabetes patients. For glucose-controlled ketoacidosis patients we found it took an average of 7.5 months for the incorrect code to be removed, while for glucose-controlled hypoglycemic patients it took an average of 9 months. We also examined blood glucose lab values and performed a case review to confirm the validity of our findings. We discuss the implications of our findings and propose future work.

Published
2015

46. Engaging hospitalized patients with personalized health information: a randomized trial of an inpatient portal.

Author

Creber, Ruth M Masterson, Grossman, Lisa V, Ryan, Beatriz, Qian, Min, Polubriaginof, Fernanda C G, Restaino, Susan, Bakken, Suzanne, Hripcsak, George, Vawdrey, David K, and Masterson Creber, Ruth M

Abstract

Objective: To determine the effects of an inpatient portal intervention on patient activation, patient satisfaction, patient engagement with health information, and 30-day hospital readmissions.Methods and Materials: From March 2014 to May 2017, we enrolled 426 English- or Spanish-speaking patients from 2 cardiac medical-surgical units at an urban academic medical center. Patients were randomized to 1 of 3 groups: 1) usual care, 2) tablet with general Internet access (tablet-only), and 3) tablet with an inpatient portal. The primary study outcome was patient activation (Patient Activation Measure-13). Secondary outcomes included all-cause readmission within 30 days, patient satisfaction, and patient engagement with health information.Results: There was no evidence of a difference in patient activation among patients assigned to the inpatient portal intervention compared to usual care or the tablet-only group. Patients in the inpatient portal group had lower 30-day hospital readmissions (5.5% vs. 12.9% tablet-only and 13.5% usual care; P = 0.044). There was evidence of a difference in patient engagement with health information between the inpatient portal and tablet-only group, including looking up health information online (89.6% vs. 51.8%; P < 0.001). Healthcare providers reported that patients found the portal useful and that the portal did not negatively impact healthcare delivery.Conclusions: Access to an inpatient portal did not significantly improve patient activation, but it was associated with looking up health information online and with a lower 30-day hospital readmission rate. These results illustrate benefit of providing hospitalized patients with real-time access to their electronic health record data while in the hospital.Trial Registration: ClinicalTrials.gov Identifier: NCT01970852. [ABSTRACT FROM AUTHOR]

Published
2019
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47. Using machine learning to parse breast pathology reports

Author

Yala, Adam, primary, Barzilay, Regina, additional, Salama, Laura, additional, Griffin, Molly, additional, Sollender, Grace, additional, Bardia, Aditya, additional, Lehman, Constance, additional, Buckley, Julliette M., additional, Coopey, Suzanne B., additional, Polubriaginof, Fernanda, additional, Garber, Judy E., additional, Smith, Barbara L., additional, Gadd, Michele A., additional, Specht, Michelle C., additional, Gudewicz, Thomas M., additional, Guidi, Anthony J., additional, Taghian, Alphonse, additional, and Hughes, Kevin S., additional

Published
2016
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48. Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records

Author

Polubriaginof, Fernanda, primary, Vanguri, Rami, additional, Quinnies, Kayla, additional, Belbin, Gillian M., additional, Yahi, Alexandre, additional, Salmasian, Hojjat, additional, Lorberbaum, Tal, additional, Nwankwo, Victor, additional, Li, Li, additional, Shervey, Mark, additional, Glowe, Patricia, additional, Ionita-Laza, Iuliana, additional, Simmerling, Mary, additional, Hripcsak, George, additional, Bakken, Suzanne, additional, Goldstein, David, additional, Kiryluk, Krzysztof, additional, Kenny, Eimear E., additional, Dudley, Joel, additional, Vawdrey, David K., additional, and Tatonetti, Nicholas P., additional

Published
2016
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49. Engaging hospitalized patients in clinical care: Study protocol for a pragmatic randomized controlled trial

Author

Masterson Creber, Ruth, primary, Prey, Jennifer, additional, Ryan, Beatriz, additional, Alarcon, Irma, additional, Qian, Min, additional, Bakken, Suzanne, additional, Feiner, Steven, additional, Hripcsak, George, additional, Polubriaginof, Fernanda, additional, Restaino, Susan, additional, Schnall, Rebecca, additional, Strong, Philip, additional, and Vawdrey, David, additional

Published
2016
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50. Association of smoking history extracted from electronic health records (EHR) using machine-learning methods and tumor characteristics in patients with lung cancer.

Author

Mukherjee, Semanti, Schroeder, Andrew, Chatterjee, Subrata, Cadley, John, Falcon, Christina J., Mehine, Miika, Bandlamudi, Chaitanya, Chatterjee, Avijit, Ladanyi, Marc, Solit, David B., Berger, Michael F., Stadler, Zsofia Kinga, Kris, Mark G., Jones, David Randolph, Schoenfeld, Adam Jacob, Polubriaginof, Fernanda C. G., Schultz, Nikolaus, Bernstein, Jonine L., Rudin, Charles M., and Offit, Kenneth

Published
2023
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