ArticleComputational and mathematical methods in medicine2021
Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Serum glucose-potassium ratio predicts inhospital mortality in patients admitted to coronary care unit.Revista da Associacao Medica Brasileira (1992) · 2024Observational
- Deep-learning-based survival prediction of patients with lower limb melanoma.Discover oncology · 2023Article
- A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Multiview Deep Forest for Overall Survival Prediction in Cancer.Computational and mathematical methods in medicine · 2023Article
- Deep-learning-based survival prediction of patients with cutaneous malignant melanoma.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs.
methodWe collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model.
resultsThe 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen (
conclusionA survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.
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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 OpenQuestion graph.