AI Medical Compendium Journal:
AMIA ... Annual Symposium proceedings. AMIA Symposium

Showing 31 to 40 of 377 articles

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances...

Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Recent work has demonstrated that large language models (LLMs) are powerful tools for clinical information extraction from unstructured text. However, existing approaches have largely ignored the extraction of numeric information such as laboratory t...

Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This research explores the integration of Artificial Intelligence (AI) into clinical decision-making in pediatric brain tumor care, specifically Adamantinomatous Craniopharyngioma (ACP). We present a user-centered design approach to introducing AI to...

Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. To address the complexity and data volume challenges involved in efficient medication information re...

Publication Type Tagging using Transformer Models and Multi-Label Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Indexing articles by their publication type and study design is essential for efficient search and filtering of the biomedical literature, but is understudied compared to indexing by MeSH topical terms. In this study, we leveraged the human-curated p...

Enhancing Wearable Sensor Data Classification Through Novel Modified- Recurrent Plot-Based Image Representation and Mixup Augmentation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it ...

Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare delivery and patient outcomes. In this work, we explore the integration of RPM features - including a...

Policy Library Redundancy Analysis Using K-means Clustering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This capstone project investigates the application of artificial intelligence (AI) techniques, specifically sentence embedding and k-means clustering using large language models, to address the challenge of policy library redundancy within a healthca...

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often requires storage and computing infrastructure that are distributed at different sites. Restrictions on...

PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Building on the success of the Segment Anything Model (SAM) in image segmentation, "PathSAM: SAM for Pathological Images in Oral Cancer Detection" addresses the unique challenges associated with diagnosing oral cancer. Although SAM is versatile, its ...