Prescriptions

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their first drug trial. Unfortunately, clinicians lack objective tools or consensus guidelines to match individual patients with the most effective therapy, frequently leading to years of uncontrolled seizures. Here, we developed machine learning models to uti...

Discriminating Inflammation from Malignancy with Short-Dynamic Patlak Parametric 18F-FDG PET/CT

Differentiating malignant from inflammatory uptake on 18F-FDG PET/CT remains a major diagnostic challenge, as standardized uptake value (SUV) lacks specificity. Dynamic acquisitions with Patlak analysis can separate metabolized from unmetabolized tracer, potentially improving discrimination. We evaluated whether short-duration dynamic FDG PET/CT with Patlak parametric imaging provides complementar...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine and Risperidone in Alzheimer’s Disease: A Real□World Cohort Study with Treatment Effect Heterogeneity Analysis

Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...

Developing Predictive Algorithms for Patient Retention Using Machine Learning and Deep Learning to Improve HIV Care in Uganda

Achieving high retention of people living with HIV (PLHIV) in care remains a challenge in Uganda, despite substantial progress towards UNAIDS 95-95-95...

Examining public acceptance of AI versus human-centric dementia care across NHS England’s dementia pathway stages

With dementia diagnoses in the UK projected to exceed one million in 2025, there is an urgent need for scalable and effective care solutions to ease p...

A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts

Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...

Domain-wide Mapping of Peer-reviewed Literature for Genetic Developmental Disorders using Machine Learning and Gene2Phenotype

Genetically determined developmental disorders (GDD) are rare, heterogeneous conditions for which clinical diagnosis increasingly depends on genomic v...

A Drug Repurposing Strategy for a New Cause of Endometrial Infertility: Unveiling Promising New Treatments

Which mechanisms of action and candidate drugs can be used to treat endometrial failure caused by molecular alterations rather than endometrial timing...

Longitudinal Patterns of Digital Parenting Restrictions and Adolescent Screen Use: Insights from the Adolescent Brain Cognitive Development (ABCD) Study

Digital parenting restrictions are widely used to manage adolescent screen use, yet it is unclear how effective these strategies remain as children ag...

Medication-Stratified Analysis of LDL-C Equation Miscalibration in Diabetes: Evidence from the All of Us Research Program and a Medication-Agnostic Machine-Learning Correction

Standard LDL-C equations were derived in cohorts largely untreated with modern combination diabetes therapies. With medication-treated patients compri...

FairCareNLP: An AI-Driven Patient Review Analyzer for Healthcare

To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to i...

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research...

Classifying and visualizing medication use in the Adolescent Brain Cognitive Development (ABCD) Study

Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...

Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward

Surgery is inherently associated with complications, making early detection the cornerstone of timely intervention and improved outcomes. Artificial i...

Integrating Protein-protein Interaction Networks and Machine Learning to Identify Biomarkers of Cancer Onset

Recent large-scale plasma proteomic studies have identified a set of biomarkers for the diagnosis of early cancer onset, but the predictive performanc...

When AI Meets the FDA: An Evaluation of Large Language Models Performance in Regulatory and Clinical Trial Data Extraction, Synthesis, and Analysis

Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...

Machine Learning–Driven Drug Optimization for Typhoid Fever Based on Patient Profiles

Typhoid fever remains a major Global public health concern, with treatment outcomes dependent on antimicrobial resistance (AMR) and patient variabilit...

Build fair machine learning models to predict adverse outcomes for Heart failure patients with preserved ejection fraction (HFpEF) and with reduced ejection fraction (HFrEF)

Heart failure (HF), including heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), remains...

ExCaPT: Explainable Cancer Prediction with Transformer-based models

Cancer remains one of the most significant global health challenges. De-spite advances in treatment, early detection remains a critical concern. The i...

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Large Language Model

To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...