用机器学习预测未完成的线上问诊结果，提升医疗分诊效率与安全。
Leveraging Machine Learning Models to Predict the Outcome of Digital Medical Triage Interviews
- 用决策树模型从不完整问诊数据中预测分诊结果
- 完全部分达100%时准确率超88%，完全部分每降低20%准确率下降约10%
- 适合关注医疗AI、分诊系统优化的研究者与开发者
现有数字分诊系统多基于问卷，依据患者提供的症状、病史和紧急程度等信息，通过预设规则确定照护等级。此类系统在问诊未完成时无法提供帮助，存在安全隐患。本研究探索使用机器学习（ML）预测未完成问诊的最终结果，以提升患者安全与服务效率。研究表明，决策树模型（尤其是LGBMClassifier和CatBoostClassifier）在完整问诊下预测准确率超过80%，且准确率与问诊完成度呈线性相关：例如，LGBMClassifier在100%完成度下准确率达88.2%，80%时为79.6%，60%时为58.9%，40%时为45.7%。TabTransformer模型在所有完成度下均实现超80%准确率，但训练耗时极长，需更强算力支持。研究揭示了决策树模型预测性能与问诊完整性之间的线性关系。
原文摘要 · Abstract (English)
Many existing digital triage systems are questionnaire-based, guiding patients to appropriate care levels based on information (e.g., symptoms, medical history, and urgency) provided by the patients answering questionnaires. Such a system often uses a deterministic model with predefined rules to determine care levels. It faces challenges with incomplete triage interviews since it can only assist patients who finish the process. In this study, we explore the use of machine learning (ML) to predict outcomes of unfinished interviews, aiming to enhance patient care and service quality. Predicting triage outcomes from incomplete data is crucial for patient safety and healthcare efficiency. Our findings show that decision-tree models, particularly LGBMClassifier and CatBoostClassifier, achieve over 80\% accuracy in predicting outcomes from complete interviews while having a linear correlation between the prediction accuracy and interview completeness degree. For example, LGBMClassifier achieves 88,2\% prediction accuracy for interviews with 100\% completeness, 79,6\% accuracy for interviews with 80\% completeness, 58,9\% accuracy for 60\% completeness, and 45,7\% accuracy for 40\% completeness. The TabTransformer model demonstrated exceptional accuracy of over 80\% for all degrees of completeness but required extensive training time, indicating a need for more powerful computational resources. The study highlights the linear correlation between interview completeness and predictive power of the decision-tree models.
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