He Yuancheng, Zhang Xiaojuan, Mei Yuexin, Qianyun Deng, Zhang Xiuqing, Chen Yuehua, Li Jie, Meng Zhou, Wei Yuehong
School of Public Health, Sun Yat-sen University, Guangzhou, China.
Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, China.
Heliyon. 2024 Sep 1;10(17):e37179. doi: 10.1016/j.heliyon.2024.e37179. eCollection 2024 Sep 15.
Ischemic stroke is a common and serious disease with economic and healthcare burdens. Predicting the unfavorable discharge outcome of patients is essential for formulating appropriate treatment strategies and providing personalized care. Therefore, this study aims to establish and validate a prediction model based on machine learning methods to accurately predict the discharge outcome of ischemic stroke patients, providing valuable information for clinical decision making.
The derivation data consisted of 964 patients from Guangdong Provincial People's Hospital and was used for training and internal validation. A favourable discharge outcome was defined as a National Institutes of Health Stroke Scale score of ≤1 or a decrease of ≥8 points compared to the admission score. A predictive model was created based on 88 medical characteristics gathered during the patient's initial admission, using nine machine learning algorithms. The model's predictive performance was compared using various evaluation metrics. The final model's feature importance was ranked and explained using the Shapley additive explanation method.
The random forest model demonstrated the greatest discriminative ability among the nine machine learning models. We created an interpretable random forest model by ranking and reducing the features based on their importance, which included eight features. In internal validations, the final model accurately predicted the discharge outcomes of ischemic stroke with AUC values of 0.903 and has been translated into a convenient tool to facilitate its utility in clinical settings.
Our explainable ML model was not only successfully developed to accurately predict discharge outcomes in patients with ischemic stroke and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique.
缺血性中风是一种常见且严重的疾病，会带来经济和医疗负担。预测患者出院时的不良结局对于制定合适的治疗策略和提供个性化护理至关重要。因此，本研究旨在建立并验证一种基于机器学习方法的预测模型，以准确预测缺血性中风患者的出院结局，为临床决策提供有价值的信息。
推导数据包括来自广东省人民医院的964例患者，用于训练和内部验证。良好的出院结局定义为美国国立卫生研究院卒中量表评分≤1分或较入院时评分降低≥8分。基于患者初次入院时收集的88项医学特征，使用9种机器学习算法创建预测模型。使用各种评估指标比较模型的预测性能。使用夏普利加性解释方法对最终模型的特征重要性进行排序和解释。
在九种机器学习模型中，随机森林模型表现出最大的判别能力。我们通过根据特征重要性对其进行排序和简化，创建了一个可解释的随机森林模型，该模型包括八个特征。在内部验证中，最终模型准确预测缺血性中风出院结局的AUC值为0.903，并且已转化为一个便捷工具，以促进其在临床环境中的应用。
我们的可解释机器学习模型不仅成功开发出来以准确预测缺血性中风患者的出院结局，而且通过对机器学习技术的直接解释减轻了对“黑箱”问题的担忧。
