AIMC Topic: Natural Language Processing

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The Best of All Worlds: A Hybrid Approach to Cohort Identification with Rules, Small and Large Language Models.

Studies in health technology and informatics
Balancing operational feasibility with the performance of natural language processing (NLP) systems is a significant challenge. This study presents a hybrid strategy to integrate manually curated rules, small language model (SLM), and large language ...

Optimizing Entity Recognition in Psychiatric Treatment Data with Large Language Models.

Studies in health technology and informatics
Extracting nuanced adverse drug reactions (ADRs) from patient self-reported messages using is pivotal but challenging, particularly given HIPAA constraints. We investigate locally deployable small LLMs-Mistral-7B, Llama-3-8B, and Gemma-7B-for ADR ext...

Plain Language Adaptations of Biomedical Text Using LLMs: Comparision of Evaluation Metrics.

Studies in health technology and informatics
This study investigated the application of Large Language Models (LLMs) for simplifying biomedical texts to enhance health literacy. Using a public dataset, which included plain language adaptations of biomedical abstracts, we developed and evaluated...

Development and Evaluation of Natural Language Processing Methods for Extracting Key Melanoma Pathology Concepts.

Studies in health technology and informatics
This study presents the development and evaluation of an annotation schema and rule-based natural language processing (NLP) system for extracting key melanoma pathology concepts from surgical pathology reports. Achieving high precision and recall, ou...

Comparative Analysis of NLP Models for Automatic LOINC Document Ontology Named Entity Recognition in Clinical Note Titles.

Studies in health technology and informatics
In order to utilize clinical notes for research studies, it is necessary to identify the most relevant notes. Mapping to the LOINC Document Ontology makes this process easier by reducing the variability of note types. We experimented with three model...

A Neural Embedding Approach to Mapping Health Concepts to Concept Unique Identifiers.

Studies in health technology and informatics
Understanding health concepts in free text is an important task in biomedical NLP. Being able to map the extracted concepts to unique concept identifiers can facilitate integration of and interoperability across biomedical informatics applications. A...

Development of an Automated Classification System for Medication-Related Incident Factors: A Practical Approach to Enhancing Patient Safety Management.

Studies in health technology and informatics
Analyzing medication-related incident reports is crucial for patient safety; however, systematically extracting the underlying factors contributing to incident occurrence remains challenging. We developed a multi-label classifier that automatically i...

Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM.

Studies in health technology and informatics
In recent decades, Electronic Health Records (EHRs) have become increasingly useful to support clinical decision-making and healthcare. EHRs usually contain heterogeneous information, such as structural data in tabular form and un-structured data in ...

Improving Zero-Shot Multiclass Classification for Narrative Reports from National Violent Death Reporting System.

Studies in health technology and informatics
The natural language processing pipeline powered by the BART model is a popular zero-shot text classification system. While the standard approach for using this pipeline can achieve impressive accuracies in many multiclass classification tasks, we be...

Exploring Prompt-Based Large Language Model (LLM) Approach for Medication Error-Related Named Entity Recognition in Medical Incident Reports.

Studies in health technology and informatics
Medication errors significantly challenge healthcare, necessitating innovative analytical methods. This study explored generative pre-trained language models (LLMs) for Named Entity Recognition (NER) in Japanese medical incident reports. We assessed ...