Correction to: Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study.
Author(s):
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
The 21st Century Cures Act Final Rule requires that certified electronic health records (EHRs) be able to export a patient's full set of electronic health information (EHI). This requirement becomes more powerful if EHI exports use interoperable application programming interfaces (APIs). We sought to advance the ecosystem, instantiating policy desiderata in a working reference implementation based on a consensus design.
Author(s): Phelan, Dylan, Gottlieb, Daniel, Mandel, Joshua C, Ignatov, Vladimir, Jones, James, Marquard, Brett, Ellis, Alyssa, Mandl, Kenneth D
DOI: 10.1093/jamia/ocae013
This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies.
Author(s): Barker, Wesley, Maisel, Natalya, Strawley, Catherine E, Israelit, Grace K, Adler-Milstein, Julia, Rosner, Benjamin
DOI: 10.1093/jamia/ocae006
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
Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort.
Author(s): Zeng, Chenjie, Schlueter, David J, Tran, Tam C, Babbar, Anav, Cassini, Thomas, Bastarache, Lisa A, Denny, Josh C
DOI: 10.1093/jamia/ocad260
The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.
Author(s): Klein, Ari Z, Banda, Juan M, Guo, Yuting, Schmidt, Ana Lucia, Xu, Dongfang, Flores Amaro, Ivan, Rodriguez-Esteban, Raul, Sarker, Abeed, Gonzalez-Hernandez, Graciela
DOI: 10.1093/jamia/ocae010
COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain.
Author(s): Tong, Jiayi, Luo, Chongliang, Sun, Yifei, Duan, Rui, Saine, M Elle, Lin, Lifeng, Peng, Yifan, Lu, Yiwen, Batra, Anchita, Pan, Anni, Wang, Olivia, Li, Ruowang, Marks-Anglin, Arielle, Yang, Yuchen, Zuo, Xu, Liu, Yulun, Bian, Jiang, Kimmel, Stephen E, Hamilton, Keith, Cuker, Adam, Hubbard, Rebecca A, Xu, Hua, Chen, Yong
DOI: 10.1093/jamia/ocad248
Understand public comfort with the use of different data types for predictive models.
Author(s): Nong, Paige, Adler-Milstein, Julia, Kardia, Sharon, Platt, Jodyn
DOI: 10.1093/jamia/ocae009
The aim of this study was to investigate how healthcare staff intermediaries support Federally Qualified Health Center (FQHC) patients' access to telehealth, how their approaches reflect cognitive load theory (CLT) and determine which approaches FQHC patients find helpful and whether their perceptions suggest cognitive load (CL) reduction.
Author(s): Williamson, Alicia K, Antonio, Marcy G, Davis, Sage, Kameswaran, Vaishnav, Dillahunt, Tawanna R, Buis, Lorraine R, Veinot, Tiffany C
DOI: 10.1093/jamia/ocad257