University of Southern California - MC 1111, 1042 Downey Way, Los Angeles, CA 90089, United States.
J Biomed Inform. 2021 Apr;116:103723. doi: 10.1016/j.jbi.2021.103723. Epub 2021 Mar 9.
Data mining is a powerful tool to reduce costs and mitigate errors in the diagnostic analysis and repair of complex engineered system, but it has yet to be applied systematically to the most complex and socially expensive system - the human body. The currently available approaches of knowledge-based and pattern-based artificial intelligence are unsuited to the iterative and often subjective nature of clinician-patient interactions. Furthermore, current electronic health records generally have poor design and low quality for such data mining. Bayesian methods have been developed to suggest multiple possible diagnoses given a set of clinical findings, but the larger problem is advising the physician on useful next steps. A new approach based on inverting Bayesian inference allows identification of the diagnostic actions that are most likely to disambiguate a differential diagnosis at each point in a patient's work-up. This can be combined with personalized cost information to suggest a cost-effective path to the clinician. Because the software is tracking the clinician's decision-making process, it can provide salient suggestions for both diagnoses and diagnostic tests in standard, coded formats that need only to be selected. This would reduce the need to type in free text, which is prone to ambiguities, omissions and errors. As the database of high-quality records grows, the scope, utility and acceptance of the system should also grow automatically, without requiring expert updating or correction.
数据挖掘是降低成本和减少复杂工程系统诊断分析和修复错误的有力工具，但尚未将其系统地应用于最复杂和社会成本最高的系统——人体。目前可用的基于知识和基于模式的人工智能方法不适合临床医生与患者交互的迭代和主观性质。此外，当前的电子健康记录通常在数据挖掘方面设计不佳且质量较低。贝叶斯方法已被开发用于根据一组临床发现提出多种可能的诊断，但更大的问题是为医生提供有用的下一步建议。一种新的基于贝叶斯推理反转的方法允许在患者检查的每个点识别最有可能消除鉴别诊断歧义的诊断操作。这可以与个性化成本信息结合使用，为临床医生建议一条具有成本效益的路径。由于软件正在跟踪临床医生的决策过程，因此它可以以标准的、已编码的格式提供有价值的诊断和诊断测试建议，而只需选择这些建议。这将减少对自由文本的需求，因为自由文本容易产生歧义、遗漏和错误。随着高质量记录数据库的增长，系统的范围、实用性和接受度也应该自动增长，而无需专家更新或更正。
