Hot topics in artificial intelligence.
Author(s): Bakken, Suzanne, Poon, Eric
DOI: 10.1093/jamia/ocae324
Author(s): Bakken, Suzanne, Poon, Eric
DOI: 10.1093/jamia/ocae324
Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.
Author(s): Correia, Rion Brattig, Rozum, Jordan C, Cross, Leonard, Felag, Jack, Gallant, Michael, Guo, Ziqi, Herr, Bruce W, Min, Aehong, Sanchez-Valle, Jon, Stungis Rocha, Deborah, Valencia, Alfonso, Wang, Xuan, Börner, Katy, Miller, Wendy, Rocha, Luis M
DOI: 10.1093/jamia/ocaf012
We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Author(s): Sritharan, Hari P, Nguyen, Harrison, van Gaal, William, Kritharides, Leonard, Chow, Clara K, Bhindi, Ravinay, ,
DOI: 10.1093/jamia/ocaf016
A proof-of-concept study aimed at designing and implementing Visual & Interactive Engagement With Electronic Records (VIEWER), a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements.
Author(s): Wang, Tao, Codling, David, Msosa, Yamiko Joseph, Broadbent, Matthew, Kornblum, Daisy, Polling, Catherine, Searle, Thomas, Delaney-Pope, Claire, Arroyo, Barbara, MacLellan, Stuart, Keddie, Zoe, Docherty, Mary, Roberts, Angus, Stewart, Robert, McGuire, Philip, Dobson, Richard, Harland, Robert
DOI: 10.1093/jamia/ocaf010
To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.
Author(s): Cauley, Michael R, Boland, Richard J, Rosenbloom, S Trent
DOI: 10.1093/jamia/ocaf001
This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Author(s): Perera, Dilruk, Liu, Siqi, See, Kay Choong, Feng, Mengling
DOI: 10.1093/jamia/ocae320
Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of [...]
Author(s): Arzideh, Kamyar, Baldini, Giulia, Winnekens, Philipp, Friedrich, Christoph M, Nensa, Felix, Idrissi-Yaghir, Ahmad, Hosch, René
DOI: 10.1055/a-2424-1989
Nephrotoxin exposure may worsen kidney injury and impair kidney recovery if continued in patients with acute kidney injury (AKI).
Author(s): Justice, Christopher M, Nevin, Connor, Neely, Rebecca L, Dilcher, Brian, Kovacic-Scherrer, Nicole, Carter-Templeton, Heather, Ostrowski, Aaron, Krafcheck, Jacob, Smith, Gordon, McCarthy, Paul, Pincavitch, Jami, Kane-Gill, Sandra, Freeman, Robert, Kellum, John A, Kohli-Seth, Roopa, Nadkarni, Girish N, Shawwa, Khaled, Sakhuja, Ankit
DOI: 10.1055/s-0044-1791822
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae307
There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area.
Author(s): Gunderson, Melissa A, Embí, Peter, Friedman, Charles P, Melton, Genevieve B
DOI: 10.1093/jamia/ocae281