Zhao Genghong, Cheng Wen, Cai Wei, Zhang Xia, Liu Jiren
School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China.
Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., No.175-2 Chuangxin Road Hunnan District, Shenyang 110167, China.
Bioengineering (Basel). 2023 Dec 26;11(1):29. doi: 10.3390/bioengineering11010029.
Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians' failure to make accurate clinical diagnoses based on patients' clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient's clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient's clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis.
诊断错误是临床诊断和治疗中的一个关键问题。在中国，临床诊断中的误诊率约为27.8%。相比之下，在全球医疗资源最发达的美国，误诊率估计平均为11.1%。据估计，每年约有79.5万美国人因诊断错误而死亡或遭受永久性残疾，其中很大一部分可归因于医生未能根据患者的临床表现做出准确的临床诊断。鉴别诊断作为临床诊断过程中不可或缺的一步，起着至关重要的作用。准确排除与患者临床表现相似的鉴别诊断是确保正确诊断和治疗的关键。目前大多数研究都集中在为特定疾病进行准确诊断，但为医生提供合理鉴别诊断辅助的研究却很少。本研究针对这一情况引入了一种新颖的解决方案，采用了与传统方法不同的机器学习技术。我们基于可解释表示和可视化计算工作流程，开发了一种用于临床循证医学的鉴别诊断推荐计算方法。该方法允许在建模中利用历史数据，并为临床医生推荐与初步诊断一起考虑的鉴别诊断。这是通过输入患者的临床表现并通过直观的可视化呈现分析结果来实现的。它可以在临床诊断过程中帮助经验不足的医生以及医疗资源有限地区的医生。研究人员在确保数据质量、安全性和隐私性的前提下，讨论了从盛京医院收集的一部分普通病历中获得的有效实验结果。这一讨论凸显了在临床实践中成功实施数据驱动的鉴别诊断推荐时解决这些问题的重要性。本研究对于寻求利用数据分析提高临床诊断中鉴别诊断效率和准确性的研究人员和从业者具有重要价值。
