Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Neil Jethani"'
Autor:
Hao Zhang, Neil Jethani, Simon Jones, Nicholas Genes, Vincent J Major, Ian S Jaffe, Anthony B Cardillo, Noah Heilenbach, Nadia Fazal Ali, Luke J Bonanni, Andrew J Clayburn, Zain Khera, Erica C Sadler, Jaideep Prasad, Jamie Schlacter, Kevin Liu, Benjamin Silva, Sophie Montgomery, Eric J Kim, Jacob Lester, Theodore M Hill, Alba Avoricani, Ethan Chervonski, James Davydov, William Small, Eesha Chakravartty, Himanshu Grover, John A Dodson, Abraham A Brody, Yindalon Aphinyanaphongs, Arjun Masurkar, Narges Razavian
Publikováno v:
PLOS Digital Health, Vol 3, Iss 12, p e0000685 (2024)
Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consist
Externí odkaz:
https://doaj.org/article/a75d72ec94b64755a5f737a01e709bd9
Autor:
Lior Jankelson, Neil Jethani, Aahlad Puli, Hao Zhang, Leonid Garber, Yindalon Aphinyanaphongs, Rajesh Ranganath
Publikováno v:
Heart Rhythm. 20:S617-S618
Publikováno v:
JAMIA Open
Objective One primary consideration when developing predictive models is downstream effects on future model performance. We conduct experiments to quantify the effects of experimental design choices, namely cohort selection and internal validation me
Autor:
Ruina Zhang, Judith S. Hochman, Carlos L. Alviar, Neil Jethani, Jeffrey S. Berger, Glenn I. Fishman, Norma Keller, Siddhant Dogra, Albert Jung, Yindalon Aphinyanaphongs, Adriana Quinones-Camacho, Ji Chen, Louai Razzouk, Nathaniel R. Smilowitz
Publikováno v:
Circulation
Publikováno v:
Proc Mach Learn Res
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce the cost of providing interpretations by learning a global selector mode
Autor:
Rajesh Ranganath, Hao Zhang, Lior Jankelson, Larry A. Chinitz, Neil Jethani, Yindalon Aphinyanaphongs
BackgroundDrug-induced QTc prolongation (diQTP) is frequent and associated with a risk of sudden cardiac death. Identifying patients at risk of diQTP can enhance monitoring and treatment plans.ObjectiveTo develop a machine learning architecture for p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f50a40be5998b506d31039bc6af22b91
https://doi.org/10.1101/2021.03.24.21254235
https://doi.org/10.1101/2021.03.24.21254235
Autor:
Siddhant Dogra, Adriana Quinones-Camacho, Yindalon Aphinyanaphongs, Albert Jung, Carlos L. Alviar, Norma Keller, Louai Razzouk, Jeffrey S. Berger, Neil Jethani, Ji Chen, Ruina Zhang, Judith S. Hochman, Glenn I. Fishman, Nathaniel R. Smilowitz
Publikováno v:
Circulation. 142
Background: Myocardial injury is frequently identified in patients with Coronavirus Disease 2019 (COVID-19). The incidence and outcome of myocardial injury at presentation and during the course of hospitalization for COVID-19 are uncertain. Methods:
Autor:
Lior Jankelson, Larry A. Chinitz, Chirag R. Barbhaiya, Michael Spinelli, Alex Kushnir, Scott Bernstein, Andrew Katz, Robert Knotts, Douglas Holmes, Rajesh Ranganath, David S. Park, Neil Jethani, Xintian Han
Publikováno v:
Heart Rhythm. 18:S438
ObjectiveThe main criteria for choosing how models are built is the subsequent effect on future (estimated) model performance. In this work, we evaluate the effects of experimental design choices on both estimated and actual model performance.Materia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0277e09dcb21d0591999d1eeb1e74b24
https://doi.org/10.1101/19008821
https://doi.org/10.1101/19008821