Predicting dire outcomes of patients with community acquired pneumonia

Authors:
Gregory F. Cooper;Vijoy Abraham;Constantin F. Aliferis;John M. Aronis;Bruce G. Buchanan;Richard Caruana;Michael J. Fine;Janine E. Janosky;Gary Livingston;Tom Mitchell;Stefano Monti;Peter Spirtes
Affiliations:
Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA;Academic Computing, Stanford University, Meyer Library, Stanford, CA;Department of Biomedical Informatics, Vanderbilt University, Nashville, TN;Department of Computer Science, University of Pittsburgh, Pittsburgh, PA;Orcas, WA;Department of Computer Science, Cornell University, Ithaca, NY;Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA;Department of Family Medicine and Clinical Epidemiology, University of Pittsburgh, Pittsburgh, PA;Department of Computer Science, University of Massachusetts Lowell, Lowell, MA;Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA;The Broad Institute of MIT and Harvard University, Cambridge, MA;Center for Automated Learning and Discovery, Carnegie Mellon University, Pittsburgh, PA and Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA
Venue:
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Year:
2005

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Abstract

Community-acquired pneumonia (CAP) is an important clinical condition with regard to patient mortality, patient morbidity, and healthcare resource utilization. The assessment of the likely clinical course of a CAP patient can significantly influence decision making about whether to treat the patient as an inpatient or as an outpatient. That decision can in turn influence resource utilization, as well as patient well being. Predicting dire outcomes, such as mortality or severe clinical complications, is a particularly important component in assessing the clinical course of patients. We used a training set of 1601 CAP patient cases to construct 11 statistical and machine-learning models that predict dire outcomes. We evaluated the resulting models on 686 additional CAP-patient cases. The primary goal was not to compare these learning algorithms as a study end point; rather, it was to develop the best model possible to predict dire outcomes. A special version of an artificial neural network (NN) model predicted dire outcomes the best. Using the 686 test cases, we estimated the expected healthcare quality and cost impact of applying the NN model in practice. The particular, quantitative results of this analysis are based on a number of assumptions that we make explicit; they will require further study and validation. Nonetheless, the general implication of the analysis seems robust, namely, that even small improvements in predictive performance for prevalent and costly diseases, such as CAP, are likely to result in significant improvements in the quality and efficiency of healthcare delivery. Therefore, seeking models with the highest possible level of predictive performance is important. Consequently, seeking ever better machine-learning and statistical modeling methods is of great practical significance.