mlsanityData to 14 Oct 2026

Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context

arXiv 2209.07859 ↗First posted 26 Aug 2022cs.IR cs.AI cs.LGcited by 15

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Abstract (arXiv)

Language Models (LMs) have performed well on biomedical natural language processing applications. In this study, we conducted some experiments to use prompt methods to extract knowledge from LMs as new knowledge Bases (LMs as KBs). However, prompting can only be used as a low bound for knowledge extraction, and perform particularly poorly on biomedical domain KBs. In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain. We design and validate a series of experiments for our Dynamic-Context-BioLAMA task. Our experiments show that the knowledge possessed by those language models can distinguish the correct knowledge from the noise knowledge in the EHR notes, and such distinguishing ability can also be used as a new metric to evaluate the amount of knowledge possessed by the model.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 26 Aug 2022.

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16 of 24 references.

References · 16

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4 more
  1. 2021Can Language Models be Biomedical Knowledge Bases?cited by 83
  2. 2020ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoningcited by 46
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Citing papers per year: 2022: 3; 2023: 9; 2024: 1; 2025: 2.

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  1. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  2. 2022Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Codingcited by 40
  3. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  4. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  5. 2022Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probingcited by 15
  6. 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
  7. 2024SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarizationcited by 12
  8. 2023Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunitiescited by 10
  9. 2023Improving Summarization with Human Editscited by 7
  10. 2022Automated Identification of Eviction Status from Electronic Health Record Notescited by 6
  11. 2023Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarizationcited by 5
  12. 2023EHRTutor: Enhancing Patient Understanding of Discharge Instructionscited by 4
3 more
  1. 2023Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generationcited by 2
  2. 2025Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Studycited by 0
  3. 2025Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspectivecited by 0