AIMC Topic: Natural Language Processing

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Efficient Training Corpus Retrieval for Large Language Model Fine Tuning: A Case Study in Cancer.

Studies in health technology and informatics
The objective is to create an automated knowledge extraction tool for cancer research that builds high-quality academic corpora for LLM fine-tuning while investigating its effectiveness in interleukin-6 and bladder cancer domains. To address the curr...

Semantic Convergence with LLMs for Head and Neck Cancer Quality Indicators.

Studies in health technology and informatics
We developed a novel method for leveraging large language models (LLM) to systematically filter and categorize large numbers of clinical quality indicators (CQI) for head and neck cancer. This was used to transform a tedious, human-resource intensive...

A Hybrid Natural Language Processing Platform for Multi-Site RWD Studies.

Studies in health technology and informatics
Real-world data (RWD) obtained from electronic medical records has become a valuable resource for healthcare research. However, integrating unstructured free-text clinical data remains a significant challenge. Although natural language processing (NL...

Enhancing and Disaggregating Native Hawaiian and Pacific Islander (NHPI) Data Using Natural Language Processing and an Expanded Race/Ethnicity Lexicon.

Studies in health technology and informatics
Native Hawaiian and Pacific Islander (NHPI) populations are often aggregated into broad racial categories, obscuring potential disparities. This study leverages an expanded race/ethnicity lexicon and natural language processing (NLP) to identify docu...

Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models.

Studies in health technology and informatics
This study evaluates fine-tuned Llama 3.2 models for extracting vaccine-related information from emergency department triage notes to support near real-time vaccine safety surveillance. Prompt engineering was used to initially create a labeled datase...

Large Language Models Can be Good Medical Annotators: A Case Study of Drug Change Detection in Japanese EHRs.

Studies in health technology and informatics
In this study, we combined automatically generated labels from large language models (LLMs) with a small number of manual annotations to classify adverse event-related treatment discontinuations in Japanese EHRs. By fine-tuning JMedRoBERTa and T5 on ...

A Performance-Based Voting Framework for Assertion Detection in Clinical Notes.

Studies in health technology and informatics
Extracting structured information from unstructured clinical text remains a critical challenge in healthcare. This study introduces a robust framework for clinical assertion detection, integrating domain-specific embeddings like BioBERT, contextualiz...

From Text to Knowledge: An End-To-End Extraction Pipeline for Clinical Information.

Studies in health technology and informatics
This study explores the use of Large Language Models (LLMs) in extracting and structuring allergic reaction data from non-English clinical free texts. Leveraging open-source models such as Llama 3.1, Qwen 2.5, and Mistral NeMo, the study utilizes 500...

AI-Assisted Detection Support for Middle Ear Diseases Using Multimodal Large Language Models.

Studies in health technology and informatics
Middle ear diseases, such as otitis media and middle ear effusion, are difficult to accurately detect in primary care. We developed an AI-powered system using Azure OpenAI's GPT-4 Vision, the first multimodal large language model (LLM) applied to ana...

Accuracy of Large Language Models in Generating Rare Disease Differential Diagnosis Using Key Clinical Features.

Studies in health technology and informatics
Generating differential diagnoses for rare disease patients can be time intensive and highly dependent on the background and training of the evaluating physicians. Large language models (LLMs) have the potential to complement this process by automati...