ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Understanding User Intent in Code-Mixed Sexual and Reproductive Health Queries in Urban India: Hierarchical Classification Approach Using Large Language Models.Journal of medical Internet research · 2026Article
- Review
- Extraction of Treatments and Responses From Non-Small Cell Lung Cancer Clinical Notes Using Natural Language Processing.JCO clinical cancer informatics · 2026Article
- Prompt-based bioinformatic pipeline generation for a multi-step metaviral workflow.Bioinformatics advances · 2026Article
- Prompt Engineering in Clinical Practice: Tutorial for Clinicians.Journal of medical Internet research · 2025Article
- Keyword-optimized template insertion for clinical note classification via prompt-based learning.BMC medical informatics and decision making · 2025Article
- When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys.NPJ digital medicine · 2025Article
- CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports.Proceedings of machine learning research · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.Journal of medical Internet research · 2025Article
- Decoding substance use disorder severity from clinical notes using a large language model.Npj mental health research · 2025Article
- Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.International journal of medical sciences · 2025Article
- CPLLM: Clinical prediction with large language models.PLOS digital health · 2024Article
- Article
- Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Prompt Engineering Paradigms for Medical Applications: Scoping Review.Journal of medical Internet research · 2024Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.Cancer medicine · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consuming to create and not publicly available. The lack of such annotated datasets is the biggest bottleneck for the development of clinical NLP systems. Zero-Shot Learning (ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique in NLP where we define task-based templates for different tasks. In this study, we developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model (PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-training-data setting. Our experiments show that HealthPrompt could effectively capture the context of clinical texts and perform well for clinical NLP tasks without any training data.
Indexed as
Identifiers
37128372PMC10148337What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.