Zobrazeno 1 - 10
of 3 906
pro vyhledávání: '"Kohane, Is"'
Autor:
Feuerriegel, Stefan, Frauen, Dennis, Melnychuk, Valentyn, Schweisthal, Jonas, Hess, Konstantin, Curth, Alicia, Bauer, Stefan, Kilbertus, Niki, Kohane, Isaac S., van der Schaar, Mihaela
Publikováno v:
Nature Medicine, vol. 30, pp. 958-968 (2024)
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating in
Externí odkaz:
http://arxiv.org/abs/2410.08770
Autor:
Kohane, Isaac
As large language models (LLMs) are deployed in high-stakes domains like healthcare, understanding how well their decision-making aligns with human preferences and values becomes crucial, especially when we recognize that there is no single gold stan
Externí odkaz:
http://arxiv.org/abs/2409.18995
Autor:
Tan, Amelia LM, Gonçalves, Rafael S, Yuan, William, Brat, Gabriel A, EHR, The Consortium for Clinical Characterization of COVID-19 by, Gentleman, Robert, Kohane, Isaac S
Objective: Integrating EHR data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The International Classification of Disea
Externí odkaz:
http://arxiv.org/abs/2407.08874
Autor:
Kohane, Isaac S, Aronow, Bruce J, Avillach, Paul, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Bradford, Robert L, Brat, Gabriel A, Cannataro, Mario, Cimino, James J, García-Barrio, Noelia, Gehlenborg, Nils, Ghassemi, Marzyeh, Gutiérrez-Sacristán, Alba, Hanauer, David A, Holmes, John H, Hong, Chuan, Klann, Jeffrey G, Loh, Ne Hooi Will, Luo, Yuan, Mandl, Kenneth D, Daniar, Mohamad, Moore, Jason H, Murphy, Shawn N, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert S, Palmer, Nathan, Patel, Lav P, Pedrera-Jiménez, Miguel, Sliz, Piotr, South, Andrew M, Tan, Amelia Li Min, Taylor, Deanne M, Taylor, Bradley W, Torti, Carlo, Vallejos, Andrew K, Wagholikar, Kavishwar B, Weber, Griffin M, Cai, Tianxi
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 3, p e22219 (2021)
Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were
Externí odkaz:
https://doaj.org/article/3185def0937d4d78ad259333d2c9ba74
Autor:
Yu, Kun-Hsing, Lee, Tsung-Lu Michael, Yen, Ming-Hsuan, Kou, S C, Rosen, Bruce, Chiang, Jung-Hsien, Kohane, Isaac S
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 8, p e16709 (2020)
BackgroundChest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rarely
Externí odkaz:
https://doaj.org/article/625b85cde8474d2cbfd9705b3c9fa8a4
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author’s location from their affiliation is crucial for understanding research netwo
Externí odkaz:
https://doaj.org/article/7a8f6b28c993440a909bfe4d900db0f8
Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") have reshaped natural language processing (NLP) through their versatility in diverse downstream tasks. However, their potential extends far beyond NLP. This paper provides a soft
Externí odkaz:
http://arxiv.org/abs/2306.11547
Autor:
Liu, Dianbo, Choi, Karmel W., Lizano, Paulo, Yuan, William, Yu, Kun-Hsing, Smoller, Jordan W., Kohane, Isaac
Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scal
Externí odkaz:
http://arxiv.org/abs/2212.10320
Publikováno v:
Proceedings of Machine Learning Research 193 (2022) 141-170
Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often,
Externí odkaz:
http://arxiv.org/abs/2212.01437
Autor:
Siyuan Chen, Amelia L. M. Tan, Maria C. Saad Menezes, Jenny F. Mao, Cassandra L. Perry, Margaret E. Vella, Vinayak V. Viswanadham, Shilpa Kobren, Susanne Churchill, Isaac S. Kohane
Publikováno v:
npj Precision Oncology, Vol 8, Iss 1, Pp 1-8 (2024)
Abstract A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme
Externí odkaz:
https://doaj.org/article/1539db3fdb144f538f381e98ca6ceadf