Latest AI and machine learning research in primary care for healthcare professionals.
Routinely collected electronic health records (EHRs) contain rich longitudinal information that enables the prediction of patient outcomes at scale. We developed GERBEHRT, a transformer model adapted from BEHRT and specifically tailored to German EHRs. GERBEHRT was pretrained on outpatient claims from more than 9 million statutorily insured patients and fine-tuned with nearly 1 million additional ...
Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, existing prediction models often fail to integrate frailty-specific biomarkers and lack interpretability for routine clinical use. To develop and internally validate an interpretable, machine learning–enhanced logistic regression model to predict prolo...
Left ventricular filling pressure (LVFP) is associated with heart failure symptoms, a key prognostic marker, and a therapeutic target, but is difficul...
Alzheimer’s disease is a progressive neurodegenerative disorder where early detection remains difficult. To address this challenge, we analysed a larg...
Pelvic floor dysfunction (PFD) is a highly prevalent and heterogeneous condition among women. The traditional view of PFD as a single clinical entity ...
Preterm birth is a syndrome that is triggered by diverse biological pathways and presents with many comorbid diseases. Although twin studies reveal a ...
Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted scr...
Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...
Diabetes mellitus (DM) is a major risk factor for acquiring infections. Metformin, the first-line treatment for type 2 DM, is associated with benefici...
Pancreatic cancer (PANC) is often diagnosed at late stages due to the absence of specific early symptoms, resulting in one of the highest cancer morta...
Arterial aging, marked by progressive vascular stiffening, is a contributor to cardiovascular disease. Photoplethysmography (PPG) waveforms offer an e...
Wearable sleep monitoring devices have proliferated over the past decade, driven by consumer interest in sleep optimization and athletic recovery trac...
Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general populat...
Dementia poses an escalating global health burden, yet its underlying mechanisms remain incompletely understood. In this large-scale, targeted metabol...
To evaluate whether incorporating baseline sleep measures from a wrist-worn activity monitor in machine learning (ML) models improved the prediction o...
Prior work showed that state-of-the-art (mid-2025) large language models (LLMs) prompted with varying batch sizes can perform well on systematic revie...
Clinical studies struggle to locate the right patients, in part because many remain undiagnosed or lack relevant labels. We develop and prospectively ...
Incomplete or incorrect causal theories are a key source of bias in machine learning (ML) algorithms. Community-engaged methodologies provide an avenu...
Cardiovascular diseases (CVD) are complex disorders involving the impaired function of blood vessels or the heart. Several risk factors contribute to ...
Simulation models inform health policy decisions by integrating data from multiple sources and forecasting outcomes when there is a lack of comprehens...