Addressing methodological and logistical challenges of using electronic health record (EHR) data for research.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae126
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocae126
Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality.
Author(s): Kim, Minwook, Kang, Donggil, Kim, Min Sun, Choe, Jeong Cheon, Lee, Sun-Hack, Ahn, Jin Hee, Oh, Jun-Hyok, Choi, Jung Hyun, Lee, Han Cheol, Cha, Kwang Soo, Jang, Kyungtae, Bong, WooR I, Song, Giltae, Lee, Hyewon
DOI: 10.1093/jamia/ocae114
We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool.
Author(s): Butler, Jorie M, Taft, Teresa, Taber, Peter, Rutter, Elizabeth, Fix, Megan, Baker, Alden, Weir, Charlene, Nevers, McKenna, Classen, David, Cosby, Karen, Jones, Makoto, Chapman, Alec, Jones, Barbara E
DOI: 10.1093/jamia/ocae112
Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility.
Author(s): Campion, Thomas R, Craven, Catherine K, Dorr, David A, Bernstam, Elmer V, Knosp, Boyd M
DOI: 10.1093/jamia/ocae111
This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation.
Author(s): Fridgeirsson, Egill A, Williams, Ross, Rijnbeek, Peter, Suchard, Marc A, Reps, Jenna M
DOI: 10.1093/jamia/ocae109
Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.
Author(s): Kawakami, Yukinobu, Matsuda, Takuya, Hidaka, Noriaki, Tanaka, Mamoru, Kimura, Eizen
DOI: 10.1093/jamia/ocae094
To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.
Author(s): Salvatore, Maxwell, Kundu, Ritoban, Shi, Xu, Friese, Christopher R, Lee, Seunggeun, Fritsche, Lars G, Mondul, Alison M, Hanauer, David, Pearce, Celeste Leigh, Mukherjee, Bhramar
DOI: 10.1093/jamia/ocae098
We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health.
Author(s): Manning, Erika Rasnick, Duan, Qing, Taylor, Stuart, Ray, Sarah, Corley, Alexandra M S, Michael, Joseph, Gillette, Ryan, Unaka, Ndidi, Hartley, David, Beck, Andrew F, Brokamp, Cole, ,
DOI: 10.1093/jamia/ocae093
Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes.
Author(s): Beaney, Thomas, Jha, Sneha, Alaa, Asem, Smith, Alexander, Clarke, Jonathan, Woodcock, Thomas, Majeed, Azeem, Aylin, Paul, Barahona, Mauricio
DOI: 10.1093/jamia/ocae091
Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.
Author(s): Jiang, Sharon, Lam, Barbara D, Agrawal, Monica, Shen, Shannon, Kurtzman, Nicholas, Horng, Steven, Karger, David R, Sontag, David
DOI: 10.1093/jamia/ocae092