AIMC Topic: Information Storage and Retrieval

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Comparative Evaluation of Pre-Trained Language Models for Biomedical Information Retrieval.

Studies in health technology and informatics
Finding relevant information in the biomedical literature increasingly depends on efficient information retrieval (IR) algorithms. Cross-Encoders, SentenceBERT, and ColBERT are algorithms based on pre-trained language models that use nuanced but comp...

Enhancing Clinical Data Extraction from Pathology Reports: A Comparative Analysis of Large Language Models.

Studies in health technology and informatics
This study evaluates the efficacy of a small large language model (sLLM) in extracting critical information from free-text pathology reports across multiple centers, addressing the challenges posed by the narrative and complex nature of these documen...

Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions.

Studies in health technology and informatics
Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction ser...

OntoBridge Versus Traditional ETL: Enhancing Data Standardization into CDM Formats Using Ontologies Within the DATOS-CAT Project.

Studies in health technology and informatics
Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compare...

Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts.

Studies in health technology and informatics
BACKGROUND: The rapid technical progress in the domain of clinical Natural Language Processing and information extraction (IE) has resulted in challenges concerning the comparability and replicability of studies.

Streamlining social media information retrieval for public health research with deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical diction...

Multimodal learning for temporal relation extraction in clinical texts.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, par...

Generating Actionable Insights from Patient Medical Records and Structured Clinical Knowledge.

Studies in health technology and informatics
While adherence to clinical guidelines improves the quality and consistency of care, personalized healthcare also requires a deep understanding of individual disease models and treatment plans. The structured preparation of medical routine data in a ...

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

Briefings in bioinformatics
As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recomm...

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We investigate the ...