Information Technology

Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study

Identifying clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help healthcare services to tailor management for each group. Large Language Models (LLMs) can utilise complex longitudinal electronic health records (EHRs) which may enable deeper insights into patterns of disease. Here, we develop a pipeline, incorporating an LLM, to generate gender-specific cluste...

Large language models improve transferability of electronic health record-based predictions across countries and coding systems

Variation in medical practices and reporting standards across healthcare systems limits the transferability of prediction models based on structured electronic health record (EHR) data. We introduce GRASP, a novel transformer-based architecture that enhances the generalizability of EHR-based prediction by embedding medical codes into a unified semantic space using a large language model. We applie...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advan...

AcuKG: a comprehensive knowledge graph for medical acupuncture

This study constructs an acupuncture knowledge graph (AcuKG) to systematically organize and represent acupuncture-related knowledge in a structured an...

Knowledge-Driven Online Multimodal Automated Phenotyping System

Though electronic health record (EHR) systems are a rich repository of clinical information with large potential, the use of EHR-based phenotyping alg...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted bi-ases, limit individual-level data sharing, and make the ...

Artificial Intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy

Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...

Open-source computational pipeline automatically flags instances of acute respiratory distress syndrome from electronic health records

Physicians, particularly intensivists, face information overload and decision fatigue, underscoring the need for automated diagnostic tools. Acute Res...

Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research

Inflammatory bowel disease (IBD) research is a dynamic field. However, the growing volume of electronic health records (EHRs) and research data presen...

Revolutionizing COPD and Asthma Management with Artificial Intelligence

The integration of artificial intelligence (AI) into the management of chronic obstructive pulmonary disease (COPD) and asthma offers significant adva...

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical scre...

Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use - A Scoping Review

Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains ...

Biomedical Text Normalization through Generative Modeling

Around 80% of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and in...

Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients

Implementing machine learning models to identify clinical deterioration on the wards is associated with improved outcomes. However, these models have ...

Identifying biomedical entities for datasets in scientific articles – A 4-step cache-augmented generation approach using GPT-4o and PubTator 3.0

The accurate annotation of biomedical entities in scientific articles is essential for effective metadata generation, ensuring data findability, acces...

Development and validation of a personalised antipsychotic selection tool for first-line treatment in severe mental illness

Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before a...

Predicting and Preventing Suicide at Entry to Mental Health Care: A Community-Engaged, Machine Learning Model Implementation

Suicide rates in the United States have increased steadily over the past twenty years, a trend coinciding with rising use of mental health services ac...

Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Case Study

Large Language Models (LLMs) have been successfully used to extract structured data from free-text radiology reports. Most of current studies were con...

A Natural Language Processing-Based Approach for Early Detection of Heart Failure Onset using Electronic Health Records

This study set out to develop and validate a risk prediction tool for the early detection of heart failure (HF) onset using real-world electronic heal...