AIMC Topic: Natural Language Processing

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LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness.

A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts.

BioInstruct: instruction tuning of large language models for biomedical natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles.

Fine-tuning large language models for rare disease concept normalization.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO).

Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE).

Impact of high-quality, mixed-domain data on the performance of medical language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliab...

CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Biomedical Knowledge Graphs play a pivotal role in various biomedical research domains. Concurrently, term clustering emerges as a crucial step in constructing these knowledge graphs, aiming to identify synonymous terms. Due to a lack of ...

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework...

Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline th...

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited cli...