AIMC Topic: Information Storage and Retrieval

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Structured LLM Augmentation for Clinical Information Extraction.

Studies in health technology and informatics
Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to ...

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...

Google Scholar as a Resource for Systematic Reviews in Clinical Medicine.

Journal of evaluation in clinical practice
BACKGROUND: Authors of systematic reviews must select, among several options, the databases for searching articles for inclusion in their analyses. Google Scholar is readily available, easy to use, and widely accepted for everyday information searche...

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID.

Entity replacement strategy for temporal knowledge graph query relaxation.

Neural networks : the official journal of the International Neural Network Society
The temporal knowledge graph (TKG) query enables the retrieval of candidate answer lists by addressing questions that involve temporal constraints, regarded as a crucial downstream task in the realm of the temporal knowledge graph. Existing methods p...

LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.

Nucleic acids research
LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 3...

Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unlocking clinical information embedded in clinical notes has been hindered to a significant degree by domain-specific and context-sensitive language. Identification of note sections and structural document elements has been shown to impro...

Enhancing search strategies for systematic reviews on drug Harms: An evaluation of the utility of ChatGPT in error detection and keyword generation.

Computers in biology and medicine
OBJECTIVE: Developing search strategies for synthesizing evidence on drug harms requires specialized expertise and knowledge. The aim of this study was to evaluate ChatGPT's ability to enhance search strategies for systematic reviews of drug harms by...

A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) gu...

Unambiguous granularity distillation for asymmetric image retrieval.

Neural networks : the official journal of the International Neural Network Society
Previous asymmetric image retrieval methods based on knowledge distillation have primarily focused on aligning the global features of two networks to transfer global semantic information from the gallery network to the query network. However, these m...