Prescriptions

A knowledge graph approach to discovering drug combination therapies across the phenome

Combining two clinically approved drugs has potential to improve treatment for common disease. But, with many thousands of combinations possible, clinically testing all pairs of drugs, with all common diseases, is not feasible. Here, we propose DRACO, a new machine learning method for discovering therapeutic drug combinations by leveraging knowledge about which health conditions each drug has been...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025) relied on somatic mutation data and emphasized long genes to guide AD drug repurposing. However, emerging evidence suggests that germline genetic variants play a more central role in AD pathogenesis. In response, we develop DeepDrug2, an enhanced AI...

Detecting Medication Mentions in Social Media Data Using Large Language Models

The automatic extraction of medication mentions from social media data is critical for pharmacovigilance and public health monitoring. In this study, ...

USING ARTIFICIAL INTELLIGENCE TO PREDICT TREATMENT OUTCOMES IN PATIENTS WITH NEUROGENIC OVERACTIVE BLADDER AND MULTIPLE SCLEROSIS

Many women with multiple sclerosis (MS) experience neurogenic overactive bladder (NOAB) characterized by urinary frequency, urinary urgency and urgenc...

SBDH-Reader: an LLM-powered method for extracting social and behavioral determinants of health from clinical notes

Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions....

Linking Trials to Publications: Enhancing Recall by Identifying Trial Registry Mentions in Full-Text

We have developed a free, public web-based tool, Trials to Publications, https://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/TrialPubLinking/trial...

COVID-19 modulates pregnancy outcomes

The COVID-19 pandemic exposed many pregnant individuals to SARS-CoV-2. Literature suggests a link between gestational COVID-19 and adverse gestational...

Interpretable Hazard Models Reveal Strong Metastasis Dependence and Feature Interaction Effects in Predicting Cancer Patient Readmission Risk

Predicting hospital readmission in cancer patients-particularly those with metastatic disease-remains a significant clinical challenge. While metastas...

Advancing the Safe Motherhood Initiative: a qualitative and sentiment analysis of local physician’s perspectives on antibiotic self-medication during pregnancy in a low- and middle-income country

Although the Safe Motherhood Initiative is currently a global priority, the implications of maternal self-medication for meeting Safe Motherhood and S...

A machine learning approach for automating review of a RxNorm medication mapping pipeline output

Medication mapping to standardized terminologies is an important prerequisite for performing analytics on a federated EHR network. TriNetX LLC operate...

Prioritising Hospital Complaints: An Innovative Tool Using Large Language Model-Assisted Content Analysis and Machine Learning Algorithms

In clinical settings, patients often express dissatisfaction through narrative speech or written text. However, most complaints management systems sti...

A conversational artificial intelligence agent for medication reconciliation and review

Medication reconciliation, the process of creating an accurate medication list for a patient, is critical to patient safety and care quality but requi...

Evaluation of a real time machine learning sepsis risk algorithm for Emergency Department waiting rooms (SAFE-WAIT)

To evaluate and compare the real-time Sepsis risk Artificial intelligence algorithm For Emergency department WAITing room (SAFE-WAIT) model with the s...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Evaluation and Application of a Probability of Success (POS) Framework in Oncology Trials

Oncology drug development encounters considerable challenges due to positive Phase I trials rarely leading to regulatory approvals, extremely competit...

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine Learning

High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causa...

Joint associations of device-measured physical activity and sleep duration with incident major adverse cardiovascular events: prospective analysis of the UK Biobank

The interaction between physical activity and sleep with cardiovascular disease remains poorly understood, despite both being key risk factors. This s...

Development of an AI-enabled predictive model to identify the ‘sick child’ at a pediatric telemedicine and medication delivery service in Haiti

One of the most difficult challenges in pediatric telemedicine is to accurately discriminate between the ‘sick’ and ‘not sick’ child, especially in re...

Privacy Protection for Chinese Electronic Medical Records Using Large Language Models: Effectiveness Evaluation and Application of LLM Models in Medical Data Tasks

The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional a...

Machine learning models to detect opioid misuse in Emergency Department patients at triage

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few r...