Constructing a Knowledge-driven and Data-driven Hybrid Decision Model for Etiological Diagnosis of Ventricular Tachycardia

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.