Barrett Laura A, Payrovnaziri Seyedeh Neelufar, Bian Jiang, He Zhe
School of Information, Florida State University, Tallahassee, Florida, USA.
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:407-416. eCollection 2019.
Heart disease remains the leading cause of death in the United States. Compared with risk assessment guidelines that require manual calculation of scores, machine learning-based prediction for disease outcomes such as mortality can be utilized to save time and improve prediction accuracy. This study built and evaluated various machine learning models to predict one-year mortality in patients diagnosed with acute myocardial infarction or post myocardial infarction syndrome in the MIMIC-III database. The results of the best performing shallow prediction models were compared to a deep feedforward neural network (Deep FNN) with back propagation. We included a cohort of 5436 admissions. Six datasets were developed and compared. The models applying Logistic Model Trees (LMT) and Simple Logistic algorithms to the combined dataset resulted in the highest prediction accuracy at 85.12% and the highest AUC at .901. In addition, other factors were observed to have an impact on outcomes as well.
心脏病仍然是美国的主要死因。与需要手动计算分数的风险评估指南相比，基于机器学习的疾病预后预测（如死亡率预测）可用于节省时间并提高预测准确性。本研究构建并评估了各种机器学习模型，以预测MIMIC-III数据库中诊断为急性心肌梗死或心肌梗死后综合征患者的一年死亡率。将表现最佳的浅层预测模型的结果与具有反向传播的深度前馈神经网络（Deep FNN）进行了比较。我们纳入了5436例入院病例。开发并比较了六个数据集。将逻辑模型树（LMT）和简单逻辑算法应用于合并数据集的模型，预测准确率最高，为85.12%，AUC最高，为0.901。此外，还观察到其他因素也会对预后产生影响。
