Prescriptions

Utilizing ChatGPT to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation

Large language models (LLMs) like ChatGPT showed great potential in aiding medical research. A heavy workload in filtering records is needed during the research process of evidence-based medicine, especially meta-analysis. However, no study tried to use LLMs to help screen records in meta-analysis. In this research, we aimed to explore the possibility of incorporating ChatGPT to facilitate the scr...

Transformers Enhance the Predictive Power of Network Medicine

Self-attention mechanisms and token embeddings behind transformers allow the extraction of complex patterns from large datasets, and enhance the predictive power over traditional machine learning models. Yet, being trained to make predictions about individual cells or genes, it is not clear if transformers can learn the inherent interaction patterns between genes, ultimately responsible for their ...

Probing Large Language Model Hidden States for Adverse Drug Reaction Knowledge

Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. However, these representations are diffic...

Bridging the Anesthesia Digital Data Gap in Low-Middle-Income Countries: Computer Vision-Ready Paper Health Records

Surgical mortality is the third leading cause of death globally, with mortality rates in Africa double those of high-income countries despite patients...

A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We developed a classifier to infer acute ischemic stroke (AIS) severity from Medicare claims using the Modified Rankin Scale (mRS) at discharge. The c...

Interrelations Between Dopaminergic-, GABAergic- and Glutamatergic Neurotransmitters in Antipsychotic-Naïve Psychosis Patients and the Association to Initial Treatment Response

Preclinical evidence points to disturbances in neural networks in psychosis involving interrelations between dopaminergic-, GABAergic- and glutamaterg...

Phenotyping Adolescent Endometriosis: Characterizing Symptom Heterogeneity Through Note- and Patient-Level Clustering

Pelvic pain (dysmenorrhea and non-menstrual) is the most common presentation of adolescent endometriosis, but symptoms vary between and within patient...

CT-based Osteoporosis Classification and Bone-Muscle Interaction Mapping Using Multiple Interpretable Machine Learning Models with the BMINet Framework

Osteoporosis progresses through stages characterized by declining bone mineral density, vertebral deterioration, and muscle atrophy, with bone-muscle ...

A Scalable Method for Validated Data Extraction from Electronic Health Records with Large Language Models

Extracting and structuring relevant clinical information from electronic health records (EHRs) remains a challenge due to the heterogeneity of systems...

Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer

Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents an...

Machine Learning Models for Dynamic Assessment of Extubation Readiness in Pediatric Critical Care

Determining the optimal timing for extubation in critically ill children remains challenging, with premature extubation leading to increased morbidity...

CONORM: Context-Aware Entity Normalization for Adverse Drug Event Detection

Adverse drug events (ADEs) are a critical aspect of patient safety and pharmacovigilance, with significant implications for patient outcomes and publi...

Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers

Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, hea...

Innovative AI models for clinical decision-making: predicting blastocyst formation and quality from time-lapse embryo images up to embryonic day 3

Accurate embryo assessment on embryonic day 3 of assisted reproductive technology (ART) is crucial for deciding whether to continue the culture until ...

Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study

A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and ca...

Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents

Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the p...

Scalable system-wide CYP2C19 pharmacogenomic testing reveals 38% excess incidence of adverse events in metabolizers receiving inappropriate prescriptions

In spite of evidence and recommendations reflecting the importance of pharmacogenomic testing, most prescriptions are still given without testing. We ...

Predicting Antiseizure Medication Outcomes in Early Diagnosed Epilepsy: A Multimodal Framework Using EEG, MRI, and Clinical Data

Accurate prediction of antiseizure medication (ASM) outcomes is crucial for optimising epilepsy treatment. We propose a multi-modal deep learning fram...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

nnDoseNet: Intuitive and Flexible Deep Learning Framework to Train and Evaluate Radiotherapy Dose Prediction Models

Radiotherapy (RT) dose optimization is often labor-intensive, requiring repeated manual adjustments to achieve clinically acceptable plans. In this wo...