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Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data

Opioid use disorder (OUD) is common in emergency departments (EDs); identification via structured computable phenotypes may miss important clinical context. Compare a computable structured OUD phenotype with a zero-shot large language model (LLM) using expert review as the reference. We retrospectively analyzed 202 adult ED encounters. Two emergency physicians independently determined OUD status w...

Enhanced Detection Rate of AI for Lung Cancer Detection on GP-Referred Chest X-rays: A Real-World Retrospective Evaluation

To assess whether an artificial intelligence (AI) chest radiograph (CXR) tool could enhance lung cancer detection on primary care–referred CXRs in the UK, and to estimate the magnitude of any improvement. From ∼280,000 primary care–referred CXRs, we identified 1,600 linked to a lung cancer diagnosis (ICD-10 C34) within six months. Missed lung cancers were defined by review of the CXR report and co...

Evaluation of T2DM Phenotyping Using Optimized Retrieval-Augmented Generation (RAG) and the Impact of Embedding Model, Context, and Prompt

Identification of patient cohorts from EHRs is challenging because ICD codes primarily serve billing and may misrepresent disease status, while key in...

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...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to ...

Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models

Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where la...

CareBot: A Pioneering Full-Process Open-Source Medical Language Model

Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. Howeve...

Zero-Shot ATC Coding with Large Language Models for Clinical Assessments

Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and oper...

Can large language models be privacy preserving and fair medical coders?

Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a commo...

DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference

This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference dir...

Representation Learning of Structured Data for Medical Foundation Models

Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effect...

INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning

Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as t...

MedCodER: A Generative AI Assistant for Medical Coding

Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural L...

Automated detection of underdiagnosed medical conditions via opportunistic imaging

Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to e...

Term Candidate Generation to Enrich Clinical Terminologies with Large Language Models.

Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigat...

What Kind of Transformer Models to Use for the ICD-10 Codes Classification Task.

Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Nat...

Towards the development of a FAIR-compliant biomedical ontology for colorectal cancer.

Despite the widespread development of ontologies in many domains of healthcare, the field of colorectal cancer (CRC) presents a notable gap considerin...

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application.

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case cl...

The potential of artificial intelligence to revolutionize health care delivery, research, and education in cardiac electrophysiology.

The field of electrophysiology (EP) has benefited from numerous seminal innovations and discoveries that have enabled clinicians to deliver therapies ...

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to addre...