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. 2005:2005:759-63.

Patient-specific models for predicting the outcomes of patients with community acquired pneumonia

Shyam Visweswaran  1 , Gregory F Cooper
Affiliations

Patient-specific models for predicting the outcomes of patients with community acquired pneumonia

Shyam Visweswaran et al. AMIA Annu Symp Proc. 2005.

Abstract

We investigated two patient-specific and four population-wide machine learning methods for predicting dire outcomes in community acquired pneumonia (CAP) patients. Predicting dire outcomes in CAP patients can significantly influence the decision about whether to admit the patient to the hospital or to treat the patient at home. Population-wide methods induce models that are trained to perform well on average on all future cases. In contrast, patient-specific methods specifically induce a model for a particular patient case. We trained the models on a set of 1601 patient cases and evaluated them on a separate set of 686 cases. One patient-specific method performed better than the population-wide methods when evaluated within a clinically relevant range of the ROC curve. Our study provides support for patient-specific methods being a promising approach for making clinical predictions.

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Figures

Figure 1
Figure 1
An example of a LBR model (or rule). The two nodes at the top represent features in the antecedent of the LBR rule that have been instantiated to their respective values in the test case. The node in the center (the outcome variable being predicted) and the three nodes at the bottom constitute the local simple Bayes classifier present in the consequent of the LBR rule.
Figure 2
Figure 2
ROC curve on the test set for the modified LBR method. The dot indicates the operating point on the curve that is discussed in the text.
See this image and copyright information in PMC

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