Summary of Methodology and Real-world Applications Of Dynamic Uncertain Causality Graph For Clinical Diagnosis with Explainability and Invariance, by Zhan Zhang et al.
Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance
by Zhan Zhang, Qin Zhang, Yang Jiao, Lin Lu, Lin Ma, Aihua Liu, Xiao Liu, Juan Zhao, Yajun Xue, Bing Wei, Mingxia Zhang, Ru Gao, Hong Zhao, Jie Lu, Fan Li, Yang Zhang, Yiming Wang, Lei Zhang, Fengwei Tian, Jie Hu, Xin Gou
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach called Dynamic Uncertain Causality Graph (DUCG) that enables explainable and invariant clinical diagnosis across various application scenarios. Unlike traditional deep learning models, DUCG is causality-driven and addresses common issues such as data collection, labeling, fitting, privacy, bias, generalization, high cost, and high energy consumption. Through a collaborative effort between clinical experts and DUCG technicians, 46 DUCG models were developed to cover 54 chief complaints, enabling the diagnosis of over 1,000 diseases without triage. The models achieved diagnostic precisions of at least 95%, with uncommon disease diagnoses achieving precision rates of at least 80%. The paper also introduces a recommendation algorithm for potential medical checks and extracts the key idea behind DUCG. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to help doctors make better diagnoses. It presents a new way called DUCG that can explain its decisions and work well in different situations. This approach addresses common problems with deep learning models, such as needing lots of data or being biased towards certain groups. By working together, doctors and computer scientists built 46 models that can diagnose over 1,000 different diseases without needing a specialist’s help. The models are very good at making accurate diagnoses, even for rare diseases. The paper also shows how this technology can be used to recommend the right medical tests and improve doctors’ skills. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Precision