# A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration

arXiv: https://arxiv.org/abs/2409.10403 · HTML: https://mlsanity.com/paper/2409.10403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [An Analysis on Large Language Models in Healthcare: A Case Study of BioBERT](https://mlsanity.com/paper/2310.07282/) (2023)

## Built on by (most cited first, 1 of 1)

- [Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis](https://mlsanity.com/paper/2512.10441/) (2025)

## Abstract (arXiv)

This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning capabilities for the task.We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the critical role of the knowledge injection module,as the removal of this module resulted in a significant drop in F1 score. The experimental results demonstrate that the proposed method not only effectively improves the accuracy of disease diagnosis but also enhances the interpretability of the predictions, providing more reliable support and evidence for clinical diagnosis.