Prescriptions

HGACL-DRP: Heterogeneous Graph Attention Dual-Perturbation Contrastive Learning Network for Drug Response Prediction

The marked heterogeneity of cancer poses a substantial challenge to precision drug therapy, resulting in considerable variability in patient responses to identical treatments. Accurately predicting drug sensitivity thus represents an urgent and critical challenge in the field of pharmacology and personalized medicine. Existing methods have limitations in addressing the complexity of multi-modal bi...

Simulated Reasoning and Self-Verification in Generalist Large Language Models for Psychiatric Diagnostic Performance: Cross-Sectional Study

Large language models (LLMs) have rapidly garnered significant interest for application in psychiatry and behavioral health. However, recent studies have identified significant shortcomings and potential risks in the performance of LLM-based systems, complicating their application to psychiatric diagnosis. Two promising approaches to addressing these challenges and improving the efficacy of these ...

Machine Learning Prediction of Pharmacogenetic Test Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: A Retrospective Cohort Study

Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due ...

Development, System Design, Safety, and Performance Metrics of a Conversational Agent for Reducing Depressive and Anxious Symptoms Based on a Large Language Model: The MHAI Study

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most ex...

Novel Epistatic Interaction Between RBMS3 and CDKN2B-AS1 in Coronary Artery Disease Risk Identified by Machine Learning Tool VariantSpark

Genome-wide association studies (GWAS) of coronary artery disease (CAD), the leading cause of mortality and morbidity globally, have identified approx...

Clinical trials in depression: Integrated collection across EU and US registries

Depression affects millions worldwide with both pharmacological and psychological therapies widely applied, both with limited treatment success. Many ...

Patient friendly summaries of oncology consultations generated by large language models - A pilot study of patient and provider satisfaction

The expanding capacity of large language models allow for improvements in patient and provider healthcare quality and experience. The medical oncology...

Privacy-preserving local language models accurately identify the presence and timing of self-harm in electronic mental health records

Self-harm, defined as intentional self-injury or self-poisoning irrespective of motivation, is the strongest risk factor for suicide and an important ...

A Machine Learning Approach to Prediction and Multimorbidity Risk Factor Identification in a low- and middle-income country

Multimorbidity, the coexistence of multiple chronic conditions, is a growing public health challenge, particularly in low- and middle-income countries...

Improving Surrogate Endpoints for Survival Prediction Through Integration of Patient-Reported Outcomes

Overall survival (OS) remains the gold standard for oncology drug approval, but measuring it requires long follow-up and is impractical in certain onc...

Automated Seizure Classification Using Multimodal Large Language Models

Accurately distinguishing between epileptic seizures (ES) and nonepileptic seizures (NES) is a significant clinical challenge that typically requires ...

Clinical Usability of Generative Artificial Intelligence for MR Safety Advice

This study investigated whether readily available, generative AI models, could be used to answer MR safety queries as an MR Safety Expert (MRSE), with...

AI-Enhanced Memory List Assessment System: Multi-Dimensional Analysis and Automated Strategy Detection for Next-Generation Cognitive Screening

Standard list-learning tasks such as the Rey Auditory Verbal Learning Test (RAVLT) and the California Verbal Learning Test (CVLT) have underpinned mem...

From claims to care: Machine learning algorithm to classify urinary tract infection cases using Swiss health insurance data

To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...

“Complex models, marginal benefits--a multi-centre development and validation study of early warning scores across 2·16 million patient admissions addressing intercurrent medical interventions”

The National Early Warning Score (NEWS) is a nationally recommended, clinically implemented system, used to prevent patient deterioration. While numer...

Physician Readiness for AI in Primary Care: A Cross-Sectional Survey on the Knowledge-Attitude Gap and Implementation Priorities in Switzerland

Primary care artificial intelligence adoption among United States (US) physicians accelerated from 38% to 66% within one year. Implementation strategi...

Speaker Role Identification in Clinical Conversations*

Patient-clinician communication research is crucial for understanding interaction dynamics and for predicting outcomes that are associated with clinic...

PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications

While medication use is common among pregnant women, medication safety remains insufficiently characterized because studies in pregnant women are chal...

A machine learning model to support the screening for methods guidance articles in MEDLINE: A performance evaluation of ASReview simulation mode

Advances in clinical research methods are frequently published in biomedical journals, but identifying these articles remains challenging due to their...

The Seizure Embedding Map: A Spatio-Temporal Transformer for Comparing Patients by Ictal Intracranial EEG Features at Scale

Planning invasive treatment for medication-resistant epilepsy relies on qualitatively interpreting seizure recordings from intracranial EEG (iEEG) rec...