Machine-learning model to predict the cause of death using a stacking ensemble method for observational data. (19th November 2020)

• Record Type: Journal Article
• Authors: Kim, Chungsoo; You, Seng Chan; Reps, Jenna M.; Cheong, Jae Youn; Park, Rae Woong
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 6(2021)
• Page Start: 1098
• Page End: 1107
• Publication Date: 2020-11-19
• Subjects: cause of death -- mortality -- machine learning -- classification -- decision support systems -- clinical; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa277
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Cause of death is used as an important outcome of clinical research; however, access to cause-of-death data is limited. This study aimed to develop and validate a machine-learning model that predicts the cause of death from the patient's last medical checkup. Materials and Methods: To classify the mortality status and each individual cause of death, we used a stacking ensemble method. The prediction outcomes were all-cause mortality, 8 leading causes of death in South Korea, and other causes. The clinical data of study populations were extracted from the national claims (n = 174 747) and electronic health records (n = 729 065) and were used for model development and external validation. Moreover, we imputed the cause of death from the data of 3 US claims databases (n = 994 518, 995 372, and 407 604, respectively). All databases were formatted to the Observational Medical Outcomes Partnership Common Data Model. Results: The generalized area under the receiver operating characteristic curve (AUROC) of the model predicting the cause of death within 60 days was 0.9511. Moreover, the AUROC of the external validation was 0.8887. Among the causes of death imputed in the Medicare Supplemental database, 11.32% of deaths were due to malignant neoplastic disease. Discussion: This study showed the potential of machine-learning models as a new alternative to address the lack of access to cause-of-death data. All processes were disclosed to maintain transparency, and the model was easily applicable to other institutions. Conclusion: A machine-learning model with competent performance was developed to predict cause of death.