Zobrazeno 1 - 10
of 495
pro vyhledávání: '"Atul J, Butte"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract The release of GPT-4 and other large language models (LLMs) has the potential to transform healthcare. However, existing research evaluating LLM performance on real-world clinical notes is limited. Here, we conduct a highly-powered study to
Externí odkaz:
https://doaj.org/article/518fbdf717084364b1ea33afc1f98785
Autor:
Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, Madhumita Sushil, Atul J. Butte, Ahmed Alaa
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-3 (2024)
Recent developments in large language models (LLMs) have unlocked opportunities for healthcare, from information synthesis to clinical decision support. These LLMs are not just capable of modeling language, but can also act as intelligent “agents
Externí odkaz:
https://doaj.org/article/d5c48901ff664ec585eb34e9723b1362
Autor:
Vivek A. Rudrapatna, Vignesh G. Ravindranath, Douglas V. Arneson, Arman Mosenia, Atul J. Butte, Shan Wang
Publikováno v:
BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-12 (2023)
Abstract Background The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data requi
Externí odkaz:
https://doaj.org/article/8da8d83d495e41b6a439f4f4fd481d54
Autor:
Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Omer Ronen, Chengzhong Ye, Euan Ashley, Atul J Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu
Publikováno v:
PLoS ONE, Vol 19, Iss 4, p e0298906 (2024)
Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wid
Externí odkaz:
https://doaj.org/article/a72759668a9746dd880de29b51bb2ca7
Autor:
Zsombor Zrubka, Gábor Kertész, László Gulácsi, János Czere, Áron Hölgyesi, Hossein Motahari Nezhad, Amir Mosavi, Levente Kovács, Atul J Butte, Márta Péntek
Publikováno v:
Journal of Medical Internet Research, Vol 26, p e47430 (2024)
BackgroundDiabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intel
Externí odkaz:
https://doaj.org/article/2059d7f9b6094306a14390b1f893a9e2
Autor:
Andy Wai Kan Yeung, Ali Torkamani, Atul J. Butte, Benjamin S. Glicksberg, Björn Schuller, Blanca Rodriguez, Daniel S. W. Ting, David Bates, Eva Schaden, Hanchuan Peng, Harald Willschke, Jeroen van der Laak, Josip Car, Kazem Rahimi, Leo Anthony Celi, Maciej Banach, Maria Kletecka-Pulker, Oliver Kimberger, Roland Eils, Sheikh Mohammed Shariful Islam, Stephen T. Wong, Tien Yin Wong, Wei Gao, Søren Brunak, Atanas G. Atanasov
Publikováno v:
Frontiers in Public Health, Vol 11 (2023)
Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare appl
Externí odkaz:
https://doaj.org/article/bf8036d28dfa46fc9da203755a4a38ec
Autor:
Erica A. Voss, Azza Shoaibi, Lana Yin Hui Lai, Clair Blacketer, Thamir Alshammari, Rupa Makadia, Kevin Haynes, Anthony G. Sena, Gowtham Rao, Sebastiaan van Sandijk, Clement Fraboulet, Laurent Boyer, Tanguy Le Carrour, Scott Horban, Daniel R. Morales, Jordi Martínez Roldán, Juan Manuel Ramírez-Anguita, Miguel A. Mayer, Marcel de Wilde, Luis H. John, Talita Duarte-Salles, Elena Roel, Andrea Pistillo, Raivo Kolde, Filip Maljković, Spiros Denaxas, Vaclav Papez, Michael G. Kahn, Karthik Natarajan, Christian Reich, Alex Secora, Evan P. Minty, Nigam H. Shah, Jose D. Posada, Maria Teresa Garcia Morales, Diego Bosca, Honorio Cadenas Juanino, Antonio Diaz Holgado, Miguel Pedrera Jiménez, Pablo Serrano Balazote, Noelia García Barrio, Selçuk Şen, Ali Yağız Üresin, Baris Erdogan, Luc Belmans, Geert Byttebier, Manu L.N.G. Malbrain, Daniel J. Dedman, Zara Cuccu, Rohit Vashisht, Atul J. Butte, Ayan Patel, Lisa Dahm, Cora Han, Fan Bu, Faaizah Arshad, Anna Ostropolets, Fredrik Nyberg, George Hripcsak, Marc A. Suchard, Dani Prieto-Alhambra, Peter R. Rijnbeek, Martijn J. Schuemie, Patrick B. Ryan
Publikováno v:
EClinicalMedicine, Vol 58, Iss , Pp 101932- (2023)
Summary: Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of
Externí odkaz:
https://doaj.org/article/699e462100af44ad885c8109c6df5465
Autor:
Daniel R. Wong, Ziqi Tang, Nicholas C. Mew, Sakshi Das, Justin Athey, Kirsty E. McAleese, Julia K. Kofler, Margaret E. Flanagan, Ewa Borys, Charles L. White, Atul J. Butte, Brittany N. Dugger, Michael J. Keiser
Publikováno v:
Acta Neuropathologica Communications, Vol 10, Iss 1, Pp 1-22 (2022)
Abstract Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, we
Externí odkaz:
https://doaj.org/article/e7fb3386bbae4059a6d4c1c8990efffb
Autor:
Siavash Zamirpour, Alan E. Hubbard, Jean Feng, Atul J. Butte, Romain Pirracchio, Andrew Bishara
Publikováno v:
Bioengineering, Vol 10, Iss 8, p 932 (2023)
Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this
Externí odkaz:
https://doaj.org/article/32c9bb30a1954f36bb7b9ffcf2eb0b13
Autor:
Andrew Bishara, Catherine Chiu, Elizabeth L. Whitlock, Vanja C. Douglas, Sei Lee, Atul J. Butte, Jacqueline M. Leung, Anne L. Donovan
Publikováno v:
BMC Anesthesiology, Vol 22, Iss 1, Pp 1-12 (2022)
Abstract Background Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record
Externí odkaz:
https://doaj.org/article/32539dd1d34b4671bcb091d65cf918b7