AIMC Topic: Electronic Health Records

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Pilot Application of a Large Language Model to Identify Hospitalisation from Unstructured Electronic Health Records in Residential Aged Care Facilities.

Studies in health technology and informatics
Older people in residential aged care facilities (RACFs) visit hospitals and utilise healthcare services more often than others in the community. Trends in hospitalization rates are essential for designing targeted aged care interventions to reduce p...

Integrating Large Language Models and Machine Learning for Enhanced Catatonia Phenotyping: A Study on Clinical Data from Electronic Health Records.

Studies in health technology and informatics
Catatonia, a complex syndrome with diagnostic challenges, was studied using a novel approach combining LightGBM and GPT-4 to enhance phenotyping from electronic health record (EHR) data. LightGBM, trained on structured data, achieved superior perform...

Designing a Healthcare Co-Pilot with Generative AI.

Studies in health technology and informatics
This paper presents our methodology for designing, testing, and evaluating a co-pilot tailored for healthcare professionals working in Spanish-speaking contexts. The co-pilot facilitates efficient access to textual information from clinical notes and...

A Novel Model for Generating Patient Laboratory Test Orders from Admission: Transformer Model Approach.

Studies in health technology and informatics
There is a growing demand for medical pseudo-data that maintains statistical utility, enabling the analysis of a wide range of medical data without compromising patient privacy. Additionally, there is a growing need for effective sequence prediction ...

Confidence-linked and uncertainty-based staged framework for phenotype validation using large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study develops and validates the confidence-linked and uncertainty-based staged (CLUES) framework by integrating large language models (LLMs) with uncertainty quantification to assist manual chart review while ensuring reliability th...

Optimizing surgical efficiency: predicting case duration of common general surgery procedures using machine learning.

Surgical endoscopy
BACKGROUND: Accurate prediction of surgical duration is critical to optimizing use of operating room resources. Currently, cases are scheduled using subjective estimates of length by surgeons, relying heavily on prior experience. This study aims to d...

Digitizing audiograms with deep learning: structured data extraction and pseudonymization for hearing big data.

Hearing research
PURPOSE: hearing loss relies on pure-tone audiometry (PTA); however, audiograms are often stored as unstructured images, limiting their integration into electronic medical records (EMRs) and common data models (CDMs). This study developed a deep lear...

[Taking digital healthcare to the next level: what practices and clinics need for digitalization!].

Urologie (Heidelberg, Germany)
BACKGROUND: Digitalization in urology is bringing about profound changes. While artificial intelligence (AI)-supported diagnoses, telemedical consultations and electronic patient files offer promising advances, challenges remain in the areas of techn...

Neurologists and Clinical Informatics: Realizing the Potential of Digital Medicine.

Seminars in neurology
Clinical informatics (CI) is an emerging field within biomedical informatics that sits at the intersection of clinical care, health systems, and health information technology (IT). CI emphasizes how individuals (neurologists, patients, staff) interac...

Differential dementia detection from multimodal brain images in a real-world dataset.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detection tasks in brain images from curated, high-quality benchmark databases, but not real-world data in hospitals.