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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.14321 |
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| _version_ | 1866929729541505024 |
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| author | Gao, Yanjun Li, Ruizhe Croxford, Emma Caskey, John Patterson, Brian W Churpek, Matthew Miller, Timothy Dligach, Dmitriy Afshar, Majid |
| author_facet | Gao, Yanjun Li, Ruizhe Croxford, Emma Caskey, John Patterson, Brian W Churpek, Matthew Miller, Timothy Dligach, Dmitriy Afshar, Majid |
| contents | Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_14321 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study Gao, Yanjun Li, Ruizhe Croxford, Emma Caskey, John Patterson, Brian W Churpek, Matthew Miller, Timothy Dligach, Dmitriy Afshar, Majid Computation and Language Artificial Intelligence Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems. |
| title | Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2308.14321 |