Correction to: Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study.
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DOI: 10.1093/jamia/ocae021
Author(s):
DOI: 10.1093/jamia/ocae021
To report on clinical informatics (CI) fellows' job search and early careers.
Author(s): Kim, Ellen, Van Cain, Melissa, Hron, Jonathan D
DOI: 10.1093/jamia/ocae008
Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment of one such system (Secure Chat).
Author(s): Kwan, Brian, Bell, John F, Longhurst, Christopher A, Goldhaber, Nicole H, Clay, Brian
DOI: 10.1093/jamia/ocad253
To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.
Author(s): Liu, Siru, McCoy, Allison B, Peterson, Josh F, Lasko, Thomas A, Sittig, Dean F, Nelson, Scott D, Andrews, Jennifer, Patterson, Lorraine, Cobb, Cheryl M, Mulherin, David, Morton, Colleen T, Wright, Adam
DOI: 10.1093/jamia/ocae019
Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.
Author(s): Scherbakov, Dmitry, Mollalo, Abolfazl, Lenert, Leslie
DOI: 10.1093/jamia/ocae023
We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2).
Author(s): Sideris, Konstantinos, Weir, Charlene R, Schmalfuss, Carsten, Hanson, Heather, Pipke, Matt, Tseng, Po-He, Lewis, Neil, Sallam, Karim, Bozkurt, Biykem, Hanff, Thomas, Schofield, Richard, Larimer, Karen, Kyriakopoulos, Christos P, Taleb, Iosif, Brinker, Lina, Curry, Tempa, Knecht, Cheri, Butler, Jorie M, Stehlik, Josef
DOI: 10.1093/jamia/ocae017
Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources.
Author(s): Wei, Wei-Qi, Rowley, Robb, Wood, Angela, MacArthur, Jacqueline, Embi, Peter J, Denaxas, Spiros
DOI: 10.1093/jamia/ocae005
Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs). However, traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining approach for label-efficient deep learning on ECG images.
Author(s): Sangha, Veer, Khunte, Akshay, Holste, Gregory, Mortazavi, Bobak J, Wang, Zhangyang, Oikonomou, Evangelos K, Khera, Rohan
DOI: 10.1093/jamia/ocae002
Author(s):
DOI: 10.1093/jamia/ocae012
Evaluate the impact of community tele-paramedicine (CTP) on patient experience and satisfaction relative to community-level indicators of health disparity.
Author(s): Daniels, Brock, McGinnis, Christina, Topaz, Leah Shafran, Greenwald, Peter, Turchioe, Meghan Reading, Creber, Ruth Marie Masterson, Sharma, Rahul
DOI: 10.1093/jamia/ocae007