Table_2_Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam.DOCX

IntroductionIn this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting.
MethodsWe selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model.
ResultsWe recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection.
ConclusionSimplified artificial intelligence could be helpful in clinical decision support in settings with limited resources.

引言
本研究开发了一款轻量化人工智能系统，旨在为资源匮乏环境下的医务人员提供临床决策支持。
方法
本研究选取了越南中部地区疾病负担沉重的7类感染性疾病：蚊媒传染病、急性胃肠炎、呼吸道感染、肺结核、脓毒症、原发性神经系统感染及病毒性肝炎。我们设计了一套问卷，用于收集疑似感染性疾病患者的当前症状与病史信息。本研究利用1129名患者的数据集开发并测试诊断模型，采用XGBoost、LightGBM及CatBoost算法构建临床决策支持人工智能系统，并通过4折交叉验证法对该模型进行验证。完成4折交叉验证后，我们在独立测试数据集上对各人工智能模型进行测试，并评估了各模型的诊断准确率。
结果
本研究最终纳入1129名患者进行分析。其中基于CatBoost算法开发的人工智能模型表现最优，准确率达87.61%，F1值为87.71。CatBoost模型针对各疾病实体的F1值介于0.80至0.97之间。脓毒症的诊断准确率最低，中枢神经系统感染的诊断准确率最高。
结论
轻量化人工智能系统可为资源匮乏环境下的临床决策提供有效支持。