ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
Predicting Mortality in Diabetic ICU Patients Using Machine Learning and Severity Indices.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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
16 citing papers in PubMed.
- Metabolic and Immune Vulnerability in Critically Ill Patients with Diabetes Mellitus.Medicina (Kaunas, Lithuania) · 2026Observational
- Explainable hierarchical clustering for patient subtyping and risk prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning.Scientific reports · 2023Article
- What, why and how to monitor blood glucose in critically ill patients.World journal of diabetes · 2023Review
- Knowledge Graph Embeddings for ICU readmission prediction.BMC medical informatics and decision making · 2023Article
- Article
- A nomogram for predicting the mortality of patients with type 2 diabetes mellitus complicated with acute kidney injury in the intensive care unit.BMC anesthesiology · 2023Article
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.PloS one · 2022Article
- Hierarchical Information Criterion for Variable Abstraction.Proceedings of machine learning research · 2021Article
- Predicting open wound mortality in the ICU using machine learning.Journal of emergency and critical care medicine (Hong Kong, China) · 2021Article
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
- Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.BMC medical informatics and decision making · 2020Article
- CAS: corpus of clinical cases in French.Journal of biomedical semantics · 2020Article
- Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Diabetes constitutes a significant health problem that leads to many long term health issues including renal, cardiovascular, and neuropathic complications. Many of these problems can result in increased health care costs, as well risk of ICU stay and mortality. To date, no published study has used predictive modeling to examine the relative influence of diabetes, diabetic health maintenance, and comorbidities on outcomes in ICU patients. Using the MIMIC-III database, machine learning and binomial logistic regression modeling were applied to predict risk of mortality. The final models achieved good fit with AUC values of 0.787 and 0.785 respectively. Additionally, this study demonstrated that robust classification can be done as a combination of five variables (HbA1c, mean glucose during stay, diagnoses upon admission, age, and type of admission) to predict risk as compared with other machine learning models that require nearly 35 variables for similar risk assessment and prediction.
Identifiers
29888089PMC5961793What OpenQuestion holds
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.