Journal ArticleDOI
Prognosis in critical care
TLDR
This article presents a brief review of the statistical methodology, multiple logistic regression, which underlies most of the models currently used in critical care, and emphasizes the importance of model calibration in this domain.Abstract:
Prognostic risk prediction models have been employed in the intensive care unit (ICU) setting since the 1980s and provide health care providers with important information to help inform decisions related to treatment and prognosis, as well as to compare outcomes across institutions. Prognostic models for critical care are among the most widely utilized and tested predictive models in healthcare. In this article, we review and compare mortality prediction models, including the APACHE (1981), SAPS (1984), APACHE-II (1985), MPM (1987), APACHE-III (1991), SAPS-II (1993), and MPM-II (1993). We emphasize the importance of model calibration in this domain. In addition, we present a brief review of the statistical methodology, multiple logistic regression, which underlies most of the models currently used in critical care.read more
Citations
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Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
TL;DR: Six use cases are presented where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation, adverse events, and treatment optimization for diseases affecting multiple organ systems.
Journal ArticleDOI
Severity of illness scoring systems in the intensive care unit.
TL;DR: The fourth-generation Acute Physiology and Chronic Health Evaluation, Simplified Acutephysiology Score 3, AcutePhysiology and chronic Health Evaluation IV, and Mortality Probability Model0 III adult prognostic models, perform well in predicting mortality.
Journal ArticleDOI
Calibrating predictive model estimates to support personalized medicine.
TL;DR: Adaptive calibration of predictions (ACP) can generate estimates that may be more suitable for individualized predictions than estimates that are calibrated using existing methods, and is not computationally expensive.
Journal ArticleDOI
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.
TL;DR: The results suggest that GLORE performs as well as LR and allows data to remain protected at their original sites and is computationally efficient.
Journal ArticleDOI
Calibration drift in regression and machine learning models for acute kidney injury.
TL;DR: Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making and should be tailored to account for variations in calibration drift across methods.
References
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Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Journal ArticleDOI
Applied Logistic Regression.
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Journal ArticleDOI
APACHE II: a severity of disease classification system.
TL;DR: The form and validation results of APACHE II, a severity of disease classification system that uses a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status, are presented.