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
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
To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability.
Author(s): Lario, Robert, Soley, Richard, White, Stephen, Butler, John, Del Fiol, Guilherme, Eilbeck, Karen, Huff, Stanley, Kawamoto, Kensaku
DOI: 10.1093/jamia/ocad242
Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary.
Author(s): Giannoula, Alexia, Comas, Mercè, Castells, Xavier, Estupiñán-Romero, Francisco, Bernal-Delgado, Enrique, Sanz, Ferran, Sala, Maria
DOI: 10.1093/jamia/ocad251
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
The increasing demands for curated, high-quality research data are driving the emergence of a novel registry type. The need to assemble, curate, and export this data grows, and the conventional simplicity of registry models is driving the need for advanced, multimodal data registries-the dawn of the next-generation registry.
Author(s): Labkoff, Steven E, Quintana, Yuri, Rozenblit, Leon
DOI: 10.1093/jamia/ocae024
Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.
Author(s): Foer, Dinah, Rubins, David M, Nguyen, Vi, McDowell, Alex, Quint, Meg, Kellaway, Mitchell, Reisner, Sari L, Zhou, Li, Bates, David W
DOI: 10.1093/jamia/ocae016
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