ArticleJournal of the American Medical Informatics Association : JAMIA2021
Machine-learning model to predict the cause of death using a stacking ensemble method for observational data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Burden of Infectious Diseases in Afghanistan in 1999 and 2023: Findings From the Global Burden of Disease 2023 Study.Cureus · 2026Article
- Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.JAMIA open · 2026Article
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.Scientific reports · 2025Article
- Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation.iScience · 2024Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.International neurourology journal · 2024Article
- Use of Virus Genotypes in Machine Learning Diagnostic Prediction Models for Cervical Cancer in Women With High-Risk Human Papillomavirus Infection.JAMA network open · 2023Article
- Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning.Analytical and bioanalytical chemistry · 2023Article
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA.Diagnostics (Basel, Switzerland) · 2023Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
- Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels.Journal of translational medicine · 2021Article
- Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.Frontiers in cardiovascular medicine · 2021Article
- Relaxation Degree Analysis Using Frontal Electroencephalogram Under Virtual Reality Relaxation Scenes.Frontiers in neuroscience · 2021Article
- FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction.Computational and structural biotechnology journal · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveCause 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
methodsTo 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.
resultsThe 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.
conclusionA machine-learning model with competent performance was developed to predict cause of death.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.