Prescriptions

A preregistered, Open Pipeline for Early Cerebral Palsy Risk Assessment from Infant Videos

Cerebral Palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via the General Movements Assessment (GMA) at 3-4 months is highly predictive for CP but relies on trained clinicians. Machine-learning-based approaches for predicting GMA score from video have shown considerable promise, but typically rely on dataset-sp...

Hypergraph-Based Doubly Robust Estimation for Causal Inference of Drug Combination Effects in Heart Failure Treatment

Disease management for heart failure with preserved ejection fraction (HFpEF) requires understanding the comparative effectiveness of real-world drug combinations rather than single agents. Standard randomized controlled trials (RCTs) for multi-drug regimens are prohibitively expensive, slow, and often infeasible at scale, motivating the use of causal machine learning methods on large-scale electr...

Drug-drug interaction identification using large language models

Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and com...

Demographics, Overlap, and Latency of Severe Cutaneous Adverse Reactions in an FDA Database

Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS-TEN), drug reaction with eosinophilia a...

A medically grounded LLM agent–based tool to detect patient safety events in medical records

Large language models (LLMs) have shown incredible promise in medicine. While LLMs may be particularly useful in areas requiring extensive review of c...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

Rx-LLM: a benchmarking suite to evaluate safe large language model performance for medication-related tasks

For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmar...

Automated Sleep Stage and Event Detection Algorithms Using Quality-Controlled PSG Annotations

To develop machine-learning models for sleep stage classification, arousal detection, and respiratory event detection from polysomnography (PSG), and ...

Machine learning augmented genome-wide meta-analysis of prescription opioid use in 860,000 individuals

Opioid analgesics are widely prescribed for pain, yet individuals vary markedly in their patterns of medical opioid use, influencing the risk of prolo...

TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical pr...

Natural language markers of drug context encoding track neural synchrony and treatment progression in heroin use disorder

Language transforms subjective internal states into observable behavior, enabling investigation of the neurocognitive dynamics central to psychiatric ...

Letter to 'Automated CT image prescription of the gallbladder using deep learning: Development, evaluation, and health promotion'.

Modeling of injury severity of distracted driving accident using statistical and machine learning models.

Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and th...

Explainable Artificial Intelligence in the Field of Drug Research.

In recent years, the widespread use of artificial intelligence (AI) and big data technologies in drug research has significantly accelerated the drug ...

DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation.

Medical image segmentation plays a crucial role in computer-aided diagnosis. By segmenting pathological tissues in medical images, doctors can observe...

The role of product art design based on a fuzzy decision support system in improving user interaction experience.

User interaction for product selection relies on its design and technical support to improve the quality of the experience. Decision support systems a...

Effects of neighborhood streetscape on the single-family housing price: Focusing on nonlinear and interaction effects using interpretable machine learning.

Previous studies using the conventional Hedonic Price Model to predict existing housing prices may have limitations in addressing the relationship bet...

Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the a...

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-...