Correction to: Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.
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DOI: 10.1093/jamia/ocae012
Author(s):
DOI: 10.1093/jamia/ocae012
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
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
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
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
Health and healthcare are increasingly dependent on internet and digital solutions. Medically underserved communities that experience health disparities are often those who are burdened by digital disparities. While digital equity and digital health equity are national priorities, there is limited evidence about how community-based organizations (CBOs) consider and develop interventions.
Author(s): Kim, Katherine K, Backonja, Uba
DOI: 10.1093/jamia/ocae020
To measure pediatrician adherence to evidence-based guidelines in the treatment of young children with attention-deficit/hyperactivity disorder (ADHD) in a diverse healthcare system using natural language processing (NLP) techniques.
Author(s): Pillai, Malvika, Posada, Jose, Gardner, Rebecca M, Hernandez-Boussard, Tina, Bannett, Yair
DOI: 10.1093/jamia/ocae001
Large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates ChatGPT family of models (GPT-3.5, GPT-4) in biomedical tasks beyond question-answering.
Author(s): Chen, Shan, Li, Yingya, Lu, Sheng, Van, Hoang, Aerts, Hugo J W L, Savova, Guergana K, Bitterman, Danielle S
DOI: 10.1093/jamia/ocad256
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
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