mlsanityData to 7 Oct 2026

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

arXiv 2203.05061 ↗First posted 9 Mar 2022cs.CL cs.AI cs.IRcited by 26

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Abstract (arXiv)

Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natural Language Processing(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 where we define task-based templates for NLP tasks. 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-data setting. Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 9 Mar 2022.

Builds on

21 of 39 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  7. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
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  9. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  10. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  11. 2019Language Models as Knowledge Bases?cited by 1,922
  12. 2019Publicly Available Clinical BERT Embeddingscited by 995
9 more
  1. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  2. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  3. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 504
  4. 2013Zero-Shot Learning Through Cross-Modal Transfercited by 237
  5. 2021Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classificationcited by 183
  6. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  7. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  8. 2021Prompt-Learning for Fine-Grained Entity Typingcited by 62
  9. 2021Classifying Long Clinical Documents with Pre-trained Transformerscited by 3

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Citing papers per year: 2022: 4; 2023: 9; 2024: 9; 2025: 4.

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  1. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  2. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  3. 2023CPLLM: Clinical Prediction with Large Language Modelscited by 34
  4. 2022Clinical Prompt Learning with Frozen Language Modelscited by 19
  5. 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processingcited by 19
  6. 2022Multi-label Few-shot ICD Coding as Autoregressive Generation with Promptcited by 16
  7. 2024Prompt engineering paradigms for medical applications: scoping review and recommendations for better practicescited by 13
  8. 2024A Survey on Responsible Generative AI: What to Generate and What Notcited by 8
  9. 2023Large Language Models for Propaganda Detectioncited by 7
  10. 2022Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detectioncited by 6
  11. 2024Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approachcited by 4
  12. 2023Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Studycited by 3
14 more
  1. 2025Large Language Models for Healthcare Text Classification: A Systematic Reviewcited by 3
  2. 2023WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categoriescited by 2
  3. 2024Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Promptingcited by 2
  4. 2022Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networkscited by 1
  5. 2024Zero- and Few-shot Named Entity Recognition and Text Expansion in Medication Prescriptions using ChatGPTcited by 1
  6. 2023Leveraging Contextual Relatedness to Identify Suicide Documentation in Clinical Notes through Zero Shot Learningcited by 0
  7. 2023Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notescited by 0
  8. 2023ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Modelscited by 0
  9. 2024A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extractioncited by 0
  10. 2024Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questionscited by 0
  11. 2024Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italiancited by 0
  12. 2025Automating Adjudication of Cardiovascular Events Using Large Language Modelscited by 0
  13. 2025Evaluating ChatGPT's Performance in Classifying Pneumonia from Chest X-Ray Imagescited by 0
  14. 2025Multi-Modal Zero-Shot Prediction of Color Trajectories in Food Dryingcited by 0