AIMC Topic: Information Storage and Retrieval

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Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion crit...

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...

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...

Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The delivery of effective targeted therapies requires comprehensive analyses of the molecular profiling of tumors and matching with clinical phenotypes in the context of existing knowledge described in biomedical literature, registries, and knowledge...

MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks such as medical question answering (QA). In addition, LLMs tend to function as "black-boxes", making it challenging to modify their beha...

Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

Journal of X-ray science and technology
OBJECTIVE: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy ...

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on larg...

The 2022 n2c2/UW shared task on extracting social determinants of health.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SD...

ChatGPT for phenotypes extraction: one model to rule them all?

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual documents (often unstructured), and feed downstream use cases with the resulting output. In genomic medici...

Information Extraction from Medical Texts with BERT Using Human-in-the-Loop Labeling.

Studies in health technology and informatics
Neural network language models, such as BERT, can be used for information extraction from medical texts with unstructured free text. These models can be pre-trained on a large corpus to learn the language and characteristics of the relevant domain an...