To construct a hybrid decision-making model that integrates knowledge-driven and data-driven approaches, and to apply it to the etiological diagnosis of ventricular tachycardia (VT).

Clinical practice guidelines, expert consensus documents, and medical literature in the field of arrhythmia diseases from 2018 to 2023 were retrieved as knowledge sources. Retrospective electronic medical record data of VT patients from Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, from 2013 to 2023 were collected as the dataset. A knowledge-driven model was constructed using a knowledge-rule-based approach to establish clinical pathways. A three-class machine learning model for VT etiology diagnosis was developed based on real-world data, and the best-performing model was selected as the representative of the data-driven approach. The machine learning model was embedded into the decision nodes of the clinical pathway in the form of custom operators, forming the hybrid model. The precision, recall, and F1 score of the three models were evaluated.

Three clinical practice guidelines were included as knowledge sources for the knowledge-driven model. A total of 1305 patient records were collected as the dataset, and five machine learning models were constructed, with the XGBoost model performing the best. The hybrid model adopted a knowledge-driven decision-making framework, embedding the XGBoost model into the decision nodes of a two-level classification. The precision, recall, and F1 scores of the three models were as follows: the knowledge-driven model achieved 80.4%, 79.1%, and 79.7%; the data-driven model achieved 88.4%, 88.5%, and 88.4%; and the hybrid model achieved 90.4%, 90.2%, and 90.3%.

The hybrid model integrating knowledge-driven and data-driven approaches demonstrated higher accuracy, and all its decision outcomes were based on evidence-based practices, aligning more closely with the actual diagnostic reasoning of clinicians. Further rigorous validation is needed to assess the feasibility of widely applying the hybrid model in the medical field.