Summary of Aipatient: Simulating Patients with Ehrs and Llm Powered Agentic Workflow, by Huizi Yu et al.
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
by Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, Jack Gallifant, Anye Shi, Xiang Li, Wenyue Hua, Mingyu Jin, Guang Chen, Yang Zhou, Zhao Li, Trisha Gupte, Ming-Li Chen, Zahra Azizi, Yongfeng Zhang, Themistocles L. Assimes, Xin Ma, Danielle S. Bitterman, Lin Lu, Lizhou Fan
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel simulated patient system, AIPatient, is proposed to advance medical education and research. Leveraging Large Language Models (LLM), the system replicates medical conditions and patient-doctor interactions with high fidelity and low cost. The system consists of an advanced knowledge graph (AIPatient KG) and a Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow. AIPatient KG is trained on Electronic Health Records (EHRs) from the Medical Information Mart for Intensive Care (MIMIC)-III database, producing a diverse cohort of patients with high knowledgebase validity. The Reasoning RAG agents perform tasks such as retrieval, query generation, abstraction, checking, rewriting, and summarization, achieving an overall accuracy of 94.15% in medical Question Answering (QA). AIPatient also exhibits high readability, robustness, and stability, making it a promising tool for applications like medical education, model evaluation, and system integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart computer that can talk to doctors and patients just like humans do. This computer is called AIPatient, and it’s designed to help train doctors and scientists without putting anyone in harm’s way. The computer uses special language models to mimic real-life medical situations, allowing doctors to practice making decisions in a safe environment. The system uses patient data from hospitals to create realistic scenarios, and it even tries to answer questions like a doctor would. AIPatient is very good at doing this job, and it could be used for many things like teaching new doctors, testing medical equipment, or even helping people understand their own medical conditions. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering » Rag » Retrieval augmented generation » Summarization
