Patient-specific models for predicting the outcomes of patients with community acquired pneumonia.
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.
Visweswaran
Shyam
S
Center for Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Pennsylvania, USA.
Cooper
Gregory F
GF
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D017714:Community-Acquired Infections; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D011014:Pneumonia / Q000150:complications / Q000401:mortality; D011379:Prognosis; D012372:ROC Curve