Latest AI and machine learning research in primary care for healthcare professionals.
Accurate and interpretable forecasting of blood glucose levels is critical for effective manage- ment of Type 2 diabetes. While complex machine learning models offer high predictive accuracy, their opacity often limits clinical applicability. This study investigates the perfor- mance of a simple, interpretable reference model: the time-of-day mean forecast. The proposed approach divides each 24-ho...
Identifying reasons for missed preventive care, such as follow-up colonoscopy after an abnormal stool-based colon cancer screening test, is critical for quality improvement initiatives. However, manual chart review to extract this information from unstructured clinical notes is time-consuming and costly. To determine whether a large language model (LLM) can accurately extract reasons for a lack of...
With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...
Heart failure with preserved ejection fraction is challenging to diagnose, precluding the initiation of prognostic medications. A deeper understanding...
We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...
Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk st...
This paper presents a hybrid model combining fuzzy logic, recursive feature elimination (RFE), and logistic regression to predict type 2 diabetes mell...
Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncert...
Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose monitoring (...
Accurate identification of direct causal (parental) variables for a target is of primary interest in many applications, especially in biomedical scien...
Early detection of cognitive impairment in assisted living is hindered by time-intensive tools like MMSE and MoCA. We present a 60-second voice-based ...
In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive t...
Type 2 diabetes (T2D) is a complex and clinically heterogeneous disease. Although clustering approaches have defined clinical subtypes, their genetic ...
Frailty is a clinical syndrome in older adults characterized by heightened vulnerability to adverse outcomes, yet it remains under-assessed in routine...
Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...
Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The Swaasa AI platform using artificial intel...
Obesity, a leading global risk factor for cardiometabolic conditions, arises from multifaceted and biologically complex mechanisms1,2. To elucidate th...
Preterm birth, defined as birth occurring before 37 weeks of gestation, poses a significant and enduring public health challenge, with substantial emo...
This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integratin...
An increase in syphilis cases in the United States and the global shortage of Benzathine Penicillin G (BPG) calls for evidence-based optimization. Con...