Search

Your search keyword '"Jannot, Anne Sophie"' showing total 473 results
Start Over Author "Jannot, Anne Sophie"
473 results on '"Jannot, Anne Sophie"'

3. An efficient joint model for high dimensional longitudinal and survival data via generic association features

Author

Nguyen, Van Tuan, Fermanian, Adeline, Guilloux, Agathe, Barbieri, Antoine, Zohar, Sarah, Jannot, Anne-Sophie, and Bussy, Simon

Subjects
Statistics - Methodology
Abstract

This paper introduces a prognostic method called FLASH that addresses the problem of joint modelling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularisation techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalised medicine or churn prediction. We develop an estimation methodology based on the EM algorithm and provide an efficient implementation. The statistical performance of the method is demonstrated both in extensive Monte Carlo simulation studies and on publicly available real-world datasets. Our method significantly outperforms the state-of-the-art joint models in predicting the latent class membership probability in terms of the C-index in a so-called ``real-time'' prediction setting, with a computational speed that is orders of magnitude faster than competing methods. In addition, our model automatically identifies significant features that are relevant from a practical perspective, making it interpretable., Comment: 31 pages, 11 figures, 4 tables

Published
2023

4. Learning the Dynamics of Sparsely Observed Interacting Systems

Author

Bleistein, Linus, Fermanian, Adeline, Jannot, Anne-Sophie, and Guilloux, Agathe

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series. The feature time series is measured on a sparse and irregular grid, while we have access to only a few points of the target time series. Once learned, we can use these dynamics to predict values of the target from the previous values of the feature time series. We frame this task as learning the solution map of a controlled differential equation (CDE). By leveraging the rich theory of signatures, we are able to cast this non-linear problem as a high-dimensional linear regression. We provide an oracle bound on the prediction error which exhibits explicit dependencies on the individual-specific sampling schemes. Our theoretical results are illustrated by simulations which show that our method outperforms existing algorithms for recovering the full time series while being computationally cheap. We conclude by demonstrating its potential on real-world epidemiological data., Comment: ICML 2023

Published
2023

13. Machine-learning-derived sepsis bundle of care

Author

Kalimouttou, Alexandre, Lerner, Ivan, Cheurfa, Chérifa, Jannot, Anne-Sophie, and Pirracchio, Romain

Subjects
Mortality -- France, Online databases -- Analysis -- Research -- Health aspects, Infection -- Research, Anti-infective agents -- Research, Machine learning -- Analysis -- Health aspects -- Research, Medical advice systems -- Health aspects -- Research -- Analysis, Septic shock -- Research, Online database, Health care industry
Abstract

Purpose Compliance to the Surviving Sepsis Campaign (SSC) guidelines is limited. This is known to be associated with increased mortality. The aim of this retrospective cohort study was to identify among the SCC guidelines the optimal bundle of recommendations that minimize 28-day mortality. Methods We used a training cohort to identify, using a least absolute shrinkage and selection operator penalized machine learning model, this bundle. Patients with sepsis/septic shock admitted to the intensive care unit (ICU) were extracted from two US databases, the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training and internal validation cohorts) and the eICU Collaborative Research Database (eICU-CRD) (external validation cohort). In the validation cohorts, we defined a bundle group that includes patients who were treated with at least all the recommendations selected in our bundle and a no-bundle group that includes patients in whom at least one recommendation from our bundle was omitted. Results All-cause 28-day mortality was the primary outcome measure. A total of 42,735 patients were included. Six recommendations (antimicrobials, balanced crystalloid, insulin therapy, corticosteroids, vasopressin, and bicarbonate therapy) were identified from the training cohort to be included in our bundle. In the propensity score-(PS)-matched internal validation cohort, the bundle group was associated with a lower mortality (OR 0.41 [0.33-0.53]; p < 0.001) compared to the no-bundle group. This was confirmed in the PS-matched external validation cohort (OR 0.75 [0.60-0.94]; p 0.02). Conclusion Our bundle of six recommendations is associated with a dramatic reduction in mortality in sepsis and septic shock. This bundle needs to be evaluated prospectively., Author(s): Alexandre Kalimouttou [sup.1] [sup.2], Ivan Lerner [sup.2] [sup.3] [sup.4], Chérifa Cheurfa [sup.5], Anne-Sophie Jannot [sup.2] [sup.3] [sup.4], Romain Pirracchio [sup.1] [sup.6] Author Affiliations: (1) grid.508487.6, 0000 0004 7885 7602, [...]

Published
2023
Full Text
View/download PDF

14. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

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.

Published
2023
Full Text
View/download PDF

15. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

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

Published
2023
Full Text
View/download PDF

18. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

Brat, Gabriel A, Weber, Griffin M, Gehlenborg, Nils, Avillach, Paul, Palmer, Nathan P, Chiovato, Luca, Cimino, James, Waitman, Lemuel R, Omenn, Gilbert S, Malovini, Alberto, Moore, Jason H, Beaulieu-Jones, Brett K, Tibollo, Valentina, Murphy, Shawn N, Yi, Sehi L’, Keller, Mark S, Bellazzi, Riccardo, Hanauer, David A, Serret-Larmande, Arnaud, Gutierrez-Sacristan, Alba, Holmes, John J, Bell, Douglas S, Mandl, Kenneth D, Follett, Robert W, Klann, Jeffrey G, Murad, Douglas A, Scudeller, Luigia, Bucalo, Mauro, Kirchoff, Katie, Craig, Jean, Obeid, Jihad, Jouhet, Vianney, Griffier, Romain, Cossin, Sebastien, Moal, Bertrand, Patel, Lav P, Bellasi, Antonio, Prokosch, Hans U, Kraska, Detlef, Sliz, Piotr, Tan, Amelia LM, Ngiam, Kee Yuan, Zambelli, Alberto, Mowery, Danielle L, Schiver, Emily, Devkota, Batsal, Bradford, Robert L, Daniar, Mohamad, Daniel, Christel, Benoit, Vincent, Bey, Romain, Paris, Nicolas, Serre, Patricia, Orlova, Nina, Dubiel, Julien, Hilka, Martin, Jannot, Anne Sophie, Breant, Stephane, Leblanc, Judith, Griffon, Nicolas, Burgun, Anita, Bernaux, Melodie, Sandrin, Arnaud, Salamanca, Elisa, Cormont, Sylvie, Ganslandt, Thomas, Gradinger, Tobias, Champ, Julien, Boeker, Martin, Martel, Patricia, Esteve, Loic, Gramfort, Alexandre, Grisel, Olivier, Leprovost, Damien, Moreau, Thomas, Varoquaux, Gael, Vie, Jill-Jênn, Wassermann, Demian, Mensch, Arthur, Caucheteux, Charlotte, Haverkamp, Christian, Lemaitre, Guillaume, Bosari, Silvano, Krantz, Ian D, South, Andrew, Cai, Tianxi, and Kohane, Isaac S

Subjects
Health Services and Systems, Health Sciences, Good Health and Well Being, Databases, Outcomes research, Viral infection, Health services and systems
Abstract

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Published
2020

19. Genetic Diagnosis Guides Treatment of Autoimmune Enteropathy

Author

Charbit-Henrion, Fabienne, Haas, Manon, Chaussade, Stanislas, Cellier, Christophe, Cerf-Bensussan, Nadine, Malamut, Georgia, Khater, Sherine, Khiat, Anis, Cording, Sascha, Parlato, Marianna, Dragon-Durey, Marie-Agnès, Beuvon, Frédéric, Brousse, Nicole, Terris, Benoît, Picard, Capucine, Fusaro, Mathieu, Rieux-Laucat, Frédéric, Stolzenberg, Marie-Claude, Jannot, Anne-Sophie, Mathian, Alexis, Allez, Matthieu, Malphettes, Marion, Fieschi, Claire, Aubourg, Alexandre, Zallot, Camille, Roblin, Xavier, Abitbol, Vered, Belle, Arthur, Wils, Pauline, Cheminant, Morgane, Matysiak-Budnik, Tamara, Vuitton, Lucine, Pouderoux, Philippe, Abramowitz, Laurent, Castelle, Martin, Suarez, Felipe, Hermine, Olivier, Ruemmele, Frank, and Mouthon, Luc

Published
2023
Full Text
View/download PDF

21. Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France

Author

Ancel, Pierre-Yves, Bauchet, Alain, Beeker, Nathanaël, Benoit, Vincent, Bernaux, Mélodie, Bellamine, Ali, Bey, Romain, Bourmaud, Aurélie, Breant, Stéphane, Burgun, Anita, Carrat, Fabrice, Caucheteux, Charlotte, Champ, Julien, Cormont, Sylvie, Daniel, Christel, Dubiel, Julien, Ducloas, Catherine, Esteve, Loic, Frank, Marie, Garcelon, Nicolas, Gramfort, Alexandre, Griffon, Nicolas, Grisel, Olivier, Guilbaud, Martin, Hassen-Khodja, Claire, Hemery, François, Hilka, Martin, Jannot, Anne Sophie, Lambert, Jerome, Layese, Richard, Leblanc, Judith, Lebouter, Léo, Lemaitre, Guillaume, Leprovost, Damien, Lerner, Ivan, Sallah, Kankoe Levi, Maire, Aurélien, Mamzer, Marie-France, Martel, Patricia, Mensch, Arthur, Moreau, Thomas, Neuraz, Antoine, Orlova, Nina, Paris, Nicolas, Rance, Bastien, Ravera, Hélène, Rozes, Antoine, Salamanca, Elisa, Sandrin, Arnaud, Serre, Patricia, Tannier, Xavier, Treluyer, Jean-Marc, Van Gysel, Damien, Varoquaux, Gaël, Vie, Jill Jen, Wack, Maxime, Wajsburt, Perceval, Wassermann, Demian, Zapletal, Eric, Hoertel, Nicolas, Sánchez-Rico, Marina, de la Muela, Pedro, Abellán, Miriam, Blanco, Carlos, Leboyer, Marion, Cougoule, Céline, Gulbins, Erich, Kornhuber, Johannes, Carpinteiro, Alexander, Becker, Katrin Anne, Vernet, Raphaël, Alvarado, Jesús M., Herrera-Morueco, Juan José, Airagnes, Guillaume, Lemogne, Cédric, and Limosin, Frédéric

Published
2023
Full Text
View/download PDF

23. Five-Year Changes in Weight and Diabetes Status After Bariatric Surgery for Craniopharyngioma-Related Hypothalamic Obesity: a Case–Control Study

Author

Faucher, Pauline, Carette, Claire, Jannot, Anne-Sophie, Gatta-Cherifi, Blandine, Van Straaten, Alexis, Piquet, Marie-Astrid, Raverot, Gerald, Alligier, Maud, Batisse, Thibault, Ziegler, Olivier, Drui, Delphine, Bretault, Marion, Farigon, Nicolas, Slim, Karem, Genser, Laurent, Poghosyan, Tigran, Vychnevskaia, Karina, Blanchard, Claire, Robert, Maud, Gronnier, Caroline, Poitou, Christine, and Czernichow, Sébastien

Published
2022
Full Text
View/download PDF

24. Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework

Author

Bussy, Simon, Veil, Raphaël, Looten, Vincent, Burgun, Anita, Gaïffas, Stéphane, Guilloux, Agathe, Ranque, Brigitte, and Jannot, Anne-Sophie

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Background: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable selection aspects separately. Second, methods are either compared within a binary outcome setting (based on an arbitrarily chosen delay) or within a survival setting, but not both. In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies. Methods: Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the binary outcome setting, we consider logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and neural network (NN); while on the survival analysis setting, we consider the Cox Proportional Hazards (PH), the CURE and the C-mix models. We then compare performances of all methods both in terms of risk prediction and variable selection, with a focus on the use of Elastic-Net regularization technique. Results: Among all assessed statistical methods assessed, the C-mix model yields the better performances in both the two considered settings, as well as interesting interpretation aspects. There is some consistency in selected covariates across methods within a setting, but not much across the two settings. Conclusions: It appears that learning withing the survival setting first, and then going back to a binary prediction using the survival estimates significantly enhance binary predictions.

Published
2018

25. Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics

Author

Bussy, Simon, Alaya, Mokhtar Z., Jannot, Anne-Sophie, and Guilloux, Agathe

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer datasets. On these high-dimensional datasets, our proposed method significantly outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.

Published
2018

28. Incidence, Risk Factors, and Outcomes of Atrial Arrhythmias in Adult Patients With Atrioventricular Septal Defect

Author

Jacquemart, Etienne, Bessière, Francis, Combes, Nicolas, Ladouceur, Magalie, Iserin, Laurence, Gardey, Kevin, Henaine, Roland, Dulac, Arnaud, Cohen, Sarah, Belli, Emre, Jannot, Anne-Sophie, Chevalier, Philippe, Ly, Reaksmei, Clavier, Sandra, Legendre, Antoine, Petit, Jérôme, Maltret, Alice, Di Filippo, Sylvie, Hascoët, Sébastien, Marijon, Eloi, and Waldmann, Victor

Published
2022
Full Text
View/download PDF

35. C-mix: a high dimensional mixture model for censored durations, with applications to genetic data

Author

Bussy, Simon, Guilloux, Agathe, Gaïffas, Stéphane, and Jannot, Anne-Sophie

Subjects
Statistics - Machine Learning
Abstract

We introduce a mixture model for censored durations (C-mix), and develop maximum likelihood inference for the joint estimation of the time distributions and latent regression parameters of the model. We consider a high-dimensional setting, with datasets containing a large number of biomedical covariates. We therefore penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model. Inference is achieved using an efficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which we provide convergence properties. We then propose a score by assessing the patients risk of early adverse event. The statistical performance of the method is examined on an extensive Monte Carlo simulation study, and finally illustrated on three genetic datasets with high-dimensional covariates. We show that our approach outperforms the state-of-the-art, namely both the CURE and Cox proportional hazards models for this task, both in terms of C-index and AUC(t).

Published
2016

36. Assessing rare diseases prevalence using literature quantification

Author

Shourick Jason, Wack Maxime, and Jannot Anne-Sophie

Subjects
Rare diseases, Bibliometrics, Prevalence, Medicine
Abstract

Abstract Introduction Estimating the prevalence of diseases is crucial for the organization of healthcare. The amount of literature on a rare pathology could help differentiate between rare and very rare diseases. The objective of this work was to evaluate to what extent the number of publications can be used to predict the prevalence of a given pathology. Methods We queried Orphanet for the global prevalence class for all conditions for which it was available. For these pathologies, we cross-referenced the Orphanet, MeSH, and OMIM vocabularies to assess the number of publication available on Pubmed using three different query strategies (one proposed in the literature, and two built specifically for this study). We first studied the association of the number of publications obtained by each of these query strategies with the prevalence class, then their predictive ability. Results Class prevalence was available for 3128 conditions, 2970 had a prevalence class

Published
2021
Full Text
View/download PDF

37. Bioprosthetic Total Artificial Heart in Autoregulated Mode Is Biologically Hemocompatible: Insights for Multimers of von Willebrand Factor

Author

Poitier, Bastien, Chocron, Richard, Peronino, Christophe, Philippe, Aurélien, Pya, Yuri, Rivet, Nadia, Richez, Ulysse, Bekbossynova, Mahabbat, Gendron, Nicolas, Grimmé, Marc, Bories, Marie Cécile, Brichet, Julie, Capel, Antoine, Rancic, Jeanne, Vedie, Benoit, Roussel, Jean Christian, Jannot, Anne-Sophie, Jansen, Piet, Carpentier, Alain, Ivak, Peter, Latremouille, Christian, Netuka, Ivan, and Smadja, David M.

Published
2022
Full Text
View/download PDF

44. Systematic Brain Natriuretic Peptide Measurement for Cardiac Screening in Patients with Severe Obesity: The OLECOEUR Study.

Author

Carette, Claire, Okamba-Belle, Diana, Hirlemann, Mélanie, Wack, Maxime, Domenge, Orianne, Jannot, Anne-Sophie, Czernichow, Sébastien, Rives-Lange, Claire, Fayol, Antoine, and Hulot, Jean-Sébastien

Subjects
BRAIN natriuretic factor, HEART failure, CARDIAC patients, VENTRICULAR septum, RIGHT heart atrium, ELECTRONIC health records
Abstract

Introduction: Heart failure (HF) is difficult to diagnose in obese patients because of cardiovascular and pulmonary comorbidities associated with physical deconditioning, all of which lead to dyspnea. Methods: The OLECOEUR study is a prospective screening for HF using systematic brain natriuretic peptide (BNP) measurement in ambulatory patients with obesity from a department of Nutrition (Paris, France). Clinical, biological, and echocardiographic data were extracted from electronic medical records. Results: We included 1,506 patients middle-aged (mean age: 47.2 ± 14.6 years old) with severe obesity (mean body mass index: 40.4 ± 6.6 kg/m2). Patients with BNP ≥35 pg/mL had left heart remodeling including thicker interventricular septum (10.4 ± 2.0 vs. 9.6 ± 1.8 mm; p = 0.0008), higher left ventricular mass (89.9 ± 24.3 vs. 77.2 ± 20.0 g/m2; p = 0.0009), and significant changes in both left and right atria consistent with a higher proportion of prior atrial fibrillation. Markers of right heart remodeling on echocardiography were also significantly higher (pulmonary artery systolic pressure: 33.3 ± 17.3 vs. 24.5 ± 6.3 mm Hg; p = 0.0002). Conclusion: The OLECOEUR study shows left and right subclinical cardiac remodeling in obese patients screened for HF with systematic dosing of BNP with usual cut-off of 35 pg/mL. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

48. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

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

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results