摘要:
目的 构建一个融合知识驱动和数据驱动的混合决策模型，并将其应用于室性心动过速的病因诊断。方法 检索2018-2023年心律失常疾病领域的临床实践指南、专家共识和医学文献作为知识源，并回顾性收集2013-2023年中国医学科学院阜外医院室性心动过速(ventricular tachycardia，VT)患者的电子病历信息作为数据集。采用基于知识规则的方法构建临床路径作为知识驱动模型;基于真实世界数据构建VT病因诊断三分类机器学习模型，并选取其中的最佳模型作为数据驱动模型代表。以临床路径为基本框架，将机器学习模型以自定义运算符的形式嵌入临床路径的决策节点中，作为混合模型。评价3种模型的精确率、召回率和F1分数。结果 共纳入3部临床实践指南作为知识驱动模型的知识源;收集了1305条患者数据作为数据集，构建了5种机器学习模型，其中XGBoost模型最佳。混合模型采用知识驱动的决策思维，分别将XGBoost模型嵌入2层分类的决策节点中。3种模型的精确率、召回率和F1分数如下:知识驱动模型为80.4%、79.1%和79.7%;数据驱动模型分别为88.4%、88.5%和88.4%;混合模型分别为90.4%、90.2%和90.3%。结论 融合知识与数据驱动的混合模型展现出更高的准确性，且混合模型的所有决策结果均基于循证证据，这更接近临床医生的实际诊断思维。未来需更严格地验证混合模型广泛应用于医学领域的可行性。
Abstract:
Objective Constructing a trustworthy and highly accurate hybrid decision model incorporating knowledge-driven and data-driven model, and applying it to the field of healthcare. Methods We collected authoritative clinical practice guidelines, expert consensus and medical literature in the field of cardiovascular diseases from 2018 to 2023 as knowledge sources and retrospectively collected electronic medical record information of patients with ventricular tachycardia (VT) at Fu Wai Hospital from 2013 to 2023 as a dataset. The knowledge-driven model constructs a clinical pathway using a knowledge rule-based approach, and the data-driven model constructs a multi-classification machine learning model for etiological diagnosis of VT based on real-world data. The hybrid model's uses the clinical pathway as the basic framework, and the machine learning model is embedded as a custom operator into the decision node of the process. The comparison metrics of the three models are precision, recall and F1 score. Results A total of three clinical guidelines were included as knowledge sources for the knowledge-driven models, as well as collected 1,305 patient data as the dataset. A total of five machine learning models were constructed and the best model was XGBoost model. The hybrid model adopts the knowledgedriven thinking, embedding the machine learning model into the decision-making node of the two layers of classification, respectively. The precision, recall and F1-scores for the knowledge-driven model were 80.4%, 79.1% and 79.7%; for machine learning model were 88.4%, 88.5%, and 88.4%; for hybrid model were 90.4%, 90.2% and 90.3%. Conclusion The results show that the strategy of integrating knowledge-driven and data-driven clinical decision-making models is feasible. Compared to the pure knowledge-driven and data-driven models, the hybrid model demonstrated higher accuracy, and all the decision-making results of the model were based on evidence-based evidence, which was closer to the actual diagnostic thinking of clinicians. The future requires more stringent validation of the hybrid model for feasibility in a broader range of medical fields.
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