Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing.
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DOI: 10.1093/jamia/ocac206
Author(s):
DOI: 10.1093/jamia/ocac206
To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.
Author(s): Ta, Casey N, Zucker, Jason E, Chiu, Po-Hsiang, Fang, Yilu, Natarajan, Karthik, Weng, Chunhua
DOI: 10.1093/jamia/ocac208
Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature.
Author(s): Hatherley, Joshua, Sparrow, Robert
DOI: 10.1093/jamia/ocac218
To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift.
Author(s): Smith, Ari J, Patterson, Brian W, Pulia, Michael S, Mayer, John, Schwei, Rebecca J, Nagarajan, Radha, Liao, Frank, Shah, Manish N, Boutilier, Justin J
DOI: 10.1093/jamia/ocac214
Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results.
Author(s): Hamidi, Bashir, Flume, Patrick A, Simpson, Kit N, Alekseyenko, Alexander V
DOI: 10.1093/jamia/ocac157
COVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19.
Author(s): Kerchberger, Vern Eric, Peterson, Josh F, Wei, Wei-Qi
DOI: 10.1093/jamia/ocac159
Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their [...]
Author(s): Elkheder, Musaab, Gonzalez-Izquierdo, Arturo, Qummer Ul Arfeen, Muhammad, Kuan, Valerie, Lumbers, R Thomas, Denaxas, Spiros, Shah, Anoop D
DOI: 10.1093/jamia/ocac158
We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout.
Author(s): Apathy, Nate C, Hare, Allison J, Fendrich, Sarah, Cross, Dori A
DOI: 10.1093/jamia/ocac211
To determine if the Conexion digital localized health information resource about diabetes and depression could increase patient activation among Hispanic low-income adults.
Author(s): Zhang, Tianmai M, Millery, Mari, Aguirre, Alejandra N, Kukafka, Rita
DOI: 10.1093/jamia/ocac213
Author(s):
DOI: 10.1093/jamia/ocac224