Latest AI and machine learning research in primary care for healthcare professionals.
Importance: Psychiatric emergency department (ED) presentations are difficult to predict using general medical risk stratification tools. Health information exchange (HIE) data may improve prediction by capturing fragmented care across settings. Objective: To develop and temporally validate a machine learning model using HIE and geospatial data to predict 30-day psychiatric ED presentation among o...
The exhaustive identification of evidence is central to systematic reviews, but the screening of titles and abstracts remains particularly labor intensive. Priority screening, an active learning approach that ranks records by estimated relevance, has emerged as an effective strategy to reduce screening workload. Its efficiency is commonly quantified using work saved over sampling at 100% recall (W...
Generative AI tools such as ChatGPT are increasingly used by the public to seek guidance on diet and physical activity for type 2 diabetes (T2D) preve...
Background: Large language models (LLMs) offer promise for systematic review data extraction, but performance in complex multidisciplinary domains and...
Diagnosed diabetes affects approximately 38.4 million Americans, but its burden is not evenly distributed across U.S. counties. Existing machine-learn...
Health-related social needs (HRSNs), such as housing instability, food insecurity, and transportation challenges, are nonmedical factors associated wi...
Background Hypertension remains one of the most challenging healthcare problems in the community. It is a common, measurable, and treatable condition ...
Introduction Stillbirth prevention requires reliable detection of potential causes for timely interventions. Currently, there is no effective screenin...
Objective: To develop and evaluate a deep learning model for five-year breast cancer risk prediction from screening breast ultrasound (BUS) examinatio...
BackgroundInitiation of emergency dialysis, often requiring temporary catheter owing to unprepared definitive vascular access, is associated with infe...
Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic ...
Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and con...
Background: Although diabetes is a potent risk factor for the development of peripheral artery disease (PAD), the effect of cumulative metabolic expos...
Background Anthropometric measures do not adequately capture heterogeneity in body fat distribution and corresponding cardiometabolic risk, whereas ma...
Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are ...
Purpose: To develop and evaluate a deep learning model for automated quantification of breast arterial calcification (BAC) on screening mammography an...
Fine-tuning can adapt pretrained medical imaging models to new clinical datasets, but device-specific domain shifts may limit generalizability. We eva...
Cannabinoids comprise a diverse class of bioactive natural products with important therapeutic potential, but efficient microbial production remains l...
Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes. So far, the research community has developed...
Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control. For Type 2 Diabetes (T2D),...