Information Technology

CLINES: Clinical LLM-based Information Extraction and Structuring Agent

Clinical narratives in electronic health records (EHRs) contain essential diagnostic, therapeutic, and temporal information that is often missing from structured fields, leaving manual chart review as the de facto standard for high-quality labels, but slow, costly, and variable, thereby constraining accurate cohort construction for clinical trials, large-scale epidemiologic studies, and the develo...

An informatics approach to profiling patient experiences using electronic health records: constructing and clustering the burden space of individuals under 65 years of age with multiple long-term conditions

Living with multiple long-term conditions (MLTC) profoundly impacts patients’ lives, affecting not only their health but also their financial, emotional, and social well-being. It can impose a significant burden on people. Here we take a novel approach, exploring the lived experience of individuals with MLTC by identifying patterns of burden—spanning physical, emotional, social, and financial doma...

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...

Development and Evaluation of Machine Learning Models to Predict Mechanical Restraint and Related Coercive Measures in Hospital Psychiatry

Use of coercive measures in psychiatric hospitals is clinically and ethically challenging. Aiming to support prevention, we developed and evaluated ma...

Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A Multi-Institutional Retrospective Cohort Study

Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electr...

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 ...

Comparison of local large language models for extraction of signs and symptoms data from electronic health records

Electronic health records (EHRs) provide a large source of data that can be used for research purposes. Extraction of information from unstructured cl...

Non-temporal tree-based models outperform temporal deep learning models in the prediction of chemotherapy-induced side effects from longitudinal laboratory data

The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical d...

Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early i...

Ocrelizumab versus Natalizumab in Relapsing-Remitting Multiple Sclerosis: A Registry-Linked Electronic Health Records Study

Ocrelizumab and natalizumab are commonly prescribed high-effectiveness disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (...

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project

Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...

Classifying polyneuropathy and myopathy patients on Electronic Health Records

Rare neuromuscular diseases such as polyneuropathy (PN) and myopathy (MY) often share symptomatic characteristics, leading to diagnostic challenges an...

A Comprehensive Investigation of Machine Learning Practices in Predictive Modeling of Alzheimer’s Disease and Related Dementias using Multisite Real-world Electronic Health Records

The irreversible progression and profound societal impact of Alzheimer’s disease and related dementias (AD/ADRD) underscore the pressing need for earl...

CLIN-SUMM: Temporal Summarization of Longitudinal Clinical Notes

Electronic health records (EHRs) contain years of longitudinal clinical notes that capture evolving patient health, treatments, and outcomes. However,...

Automated Risk Assessment of Amputation in Patients with Peripheral Artery Disease

Peripheral artery disease (PAD) affects over eight million Americans and is a leading cause of non-traumatic lower extremity amputation in the United ...

The Vertebrate Breed Ontology: Toward Effective Breed Data Standardization.

BACKGROUND: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; t...

Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data.

OBJECTIVE: To develop a non-invasive, radiation-free model for early colorectal adenoma prediction using clinical electronic medical record (EMR) data...

Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.

OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribut...

Research on APT groups malware classification based on TCN-GAN.

Advanced Persistent Threat (APT) malware attacks, characterized by their stealth, persistence, and high destructiveness, have become a critical focus ...