Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study

Abstract

Background Cardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. Objective This study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. Methods The data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of deceased patients in the data set, a separate experiment using the Synthetic Minority Oversampling Technique (SMOTE) was conducted to enrich the data. Results Regarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. Conclusions Compared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

“…AI technology has shown promising potential for outcome prediction, pattern recognition, and diagnostic classification to support clinicians in decision-making during diagnosis and treatment. 4 , 7 , 13 – 15 Different types of innovative AI technologies for clinical-decision support have been developed 4 , 16 and tested in clinical trials, for example, in sepsis care, 17 – 21 cardiovascular care, 22 – 24 and COVID-19 care 25 , 26 to predict stages of disease, assess prognosis, assist decision-making, and predict mortality risk. 19 – 22 , 25 , 27 , 28 One potentially effective use of predictive analytics in the emergency department is to support decisions on which patient should be admitted to a hospital ward and which patient could be safely discharged or displaced, optimizing the use of healthcare recourses and the quality of healthcare.…”

“… 4 , 7 , 13 – 15 Different types of innovative AI technologies for clinical-decision support have been developed 4 , 16 and tested in clinical trials, for example, in sepsis care, 17 – 21 cardiovascular care, 22 – 24 and COVID-19 care 25 , 26 to predict stages of disease, assess prognosis, assist decision-making, and predict mortality risk. 19 – 22 , 25 , 27 , 28 One potentially effective use of predictive analytics in the emergency department is to support decisions on which patient should be admitted to a hospital ward and which patient could be safely discharged or displaced, optimizing the use of healthcare recourses and the quality of healthcare. 8 , 20 , 28 – 30 Such use of AI technology would transform current practice in the emergency department and would address current constraints with a heavy workload and stress on healthcare professionals.…”