Rezultati - Razavian, Narges

Artificial Intelligence Explained for Nonexperts od Razavian, Narges, Knoll, Florian, Geras, Krzysztof J.

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Learning generative models of molecular dynamics od Razavian, Narges Sharif, Kamisetty, Hetunandan, Langmead, Christopher J

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Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models od Hong, Runyu, Liu, Wenke, DeLair, Deborah, Razavian, Narges, Fenyö, David

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Association of Psychiatric Disorders With Mortality Among Patients With COVID-19 od Nemani, Katlyn, Li, Chenxiang, Olfson, Mark, Blessing, Esther M., Razavian, Narges, Chen, Ji, Petkova, Eva, Goff, Donald C.

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Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial od Major, Vincent J., Jones, Simon A., Razavian, Narges, Bagheri, Ashley, Mendoza, Felicia, Stadelman, Jay, Horwitz, Leora I., Austrian, Jonathan, Aphinyanaphongs, Yindalon

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Predicting childhood obesity using electronic health records and publicly available data od Hammond, Robert, Athanasiadou, Rodoniki, Curado, Silvia, Aphinyanaphongs, Yindalon, Abrams, Courtney, Messito, Mary Jo, Gross, Rachel, Katzow, Michelle, Jay, Melanie, Razavian, Narges, Elbel, Brian

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Correction: Predicting childhood obesity using electronic health records and publicly available data od Hammond, Robert, Athanasiadou, Rodoniki, Curado, Silvia, Aphinyanaphongs, Yindalon, Abrams, Courtney, Messito, Mary Jo, Gross, Rachel, Katzow, Michelle, Jay, Melanie, Razavian, Narges, Elbel, Brian

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Deep learning and pathomics analyses reveal cell nuclei as important features for mutation prediction of BRAF-mutated melanomas od Kim, Randie H., Nomikou, Sofia, Coudray, Nicolas, Jour, George, Dawood, Zarmeena, Hong, Runyu, Esteva, Eduardo, Sakellaropoulos, Theodore, Donnelly, Douglas, Moran, Una, Hatzimemos, Aristides, Weber, Jeffrey S., Razavian, Narges, Aifantis, Iannis, Fenyo, David, Snuderl, Matija, Shapiro, Richard, Berman, Russell S., Osman, Iman, Tsirigos, Aristotelis

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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department od Shamout, Farah E., Shen, Yiqiu, Wu, Nan, Kaku, Aakash, Park, Jungkyu, Makino, Taro, Jastrzębski, Stanisław, Witowski, Jan, Wang, Duo, Zhang, Ben, Dogra, Siddhant, Cao, Meng, Razavian, Narges, Kudlowitz, David, Azour, Lea, Moore, William, Lui, Yvonne W., Aphinyanaphongs, Yindalon, Fernandez-Granda, Carlos, Geras, Krzysztof J.

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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department od Shamout, Farah E., Shen, Yiqiu, Wu, Nan, Kaku, Aakash, Park, Jungkyu, Makino, Taro, Jastrzębski, Stanisław, Witowski, Jan, Wang, Duo, Zhang, Ben, Dogra, Siddhant, Cao, Meng, Razavian, Narges, Kudlowitz, David, Azour, Lea, Moore, William, Lui, Yvonne W., Aphinyanaphongs, Yindalon, Fernandez-Granda, Carlos, Geras, Krzysztof J.

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A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients od Razavian, Narges, Major, Vincent J., Sudarshan, Mukund, Burk-Rafel, Jesse, Stella, Peter, Randhawa, Hardev, Bilaloglu, Seda, Chen, Ji, Nguy, Vuthy, Wang, Walter, Zhang, Hao, Reinstein, Ilan, Kudlowitz, David, Zenger, Cameron, Cao, Meng, Zhang, Ruina, Dogra, Siddhant, Harish, Keerthi B., Bosworth, Brian, Francois, Fritz, Horwitz, Leora I., Ranganath, Rajesh, Austrian, Jonathan, Aphinyanaphongs, Yindalon

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