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Electronic Health Records

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Discontinuous named entities in clinical text: A systematic literature review.

Journal of biomedical informatics
OBJECTIVE: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identi...

Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems.

Circulation. Genomic and precision medicine
BACKGROUND: While universal screening for Lipoprotein(a) [Lp(a)] is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a ...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

International journal of medical informatics
BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique vulnerabilities and etiologies. However, existing approaches fail to fully utilize regional heterogen...

Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.

Computers in biology and medicine
Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these conditions is crucial for timely intervention and prevention of adverse outcomes. In this study, we...

National survey on data governance and digital surgery: Challenges and opportunities for surgeons in the era of artificial intelligence.

Cirugia espanola
INTRODUCTION: This study evaluates the knowledge of Spanish surgeons regarding data governance and Digital Surgery, their usage, errors, and training deficiencies, as well as differences in knowledge between those who perform robotic surgery and thos...

Large language models vs human for classifying clinical documents.

International journal of medical informatics
BACKGROUND: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and...

Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured ...

Piloting an automated query and scoring system to facilitate APDS patient identification from health systems.

Frontiers in immunology
INTRODUCTION: Patients with activated PI3Kδ syndrome (APDS) may elude diagnoses for nearly a decade. Methods to hasten the identification of these patients, and other patients with inborn errors of immunity (IEIs), are needed. We sought to demonstrat...

Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation.

BMJ health & care informatics
OBJECTIVES: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.