APACHE II: a severity of disease classification system.

By W. Knaus, E. Draper, D. Wagner, Jack E. Zimmerman. 1985

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Citations in the corpus

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “The Thrombolysis In Myocardial Infarction (TIMI) Score [Antman et al. (2000)], Apache II score for infant mortality in the ICU [Knaus et al. (1985)], the CURB-65 score for predicting mortality in community-acquired pneumonia [Lim et al. (2003)] and the CHADS2 score [Gage et al. (2001)] are examples…”
Falling Rule Lists (2014, methodology): “These manually-created risk assessment tools are used in possibly every hospital; e.g., the TIMI scores, CHADS 2 score, Apache scores, and the Ranson score, to name a few (Antman et al. , 2000; Morrow et al. , 2000; Gage et al. , 2001; Knaus et al. , 1981, 1985, 1991; Ranson et al. , 1974).”
Supersparse Linear Integer Models for Predictive Scoring Systems (2013, methodology): “See, for instance, these commonly used scoring systems: (Gage et al. 2001; Le Gall et al. 1984; Le Gall, Lemeshow, and Saulnier 1993; Knaus et al. 1985).”
Improving palliative care with deep learning (2017, background): “The APACHE-II (Acute Physiology, Age, Chronic Health Evaluation) Score predicts hospital mortality risk for critically ill hospitalized adults in the ICU [17].”
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018, methodology): “Each sample in the time-grid was then labeled using a dynamic variant of the APACHE score [20], which is a proxy for the instantaneous physiological state of a patient in the ICU.”
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, background): “The setting is multi-class classification with labels 1, . . . , L and training data {(x i , y i )} n i=1 , where x i ∈ R d are the features of observation i and y i ∈ {1, . . . , L} are the labels.”