Repeated prognosis in the intensive care: how well do physicians and temporal models perform?

Authors:
Lilian Minne;Evert De Jonge;Ameen Abu-Hanna
Affiliations:
Academic Medical Center, Department of Medical Informatics, Amsterdam, The Netherlands;Leiden University Medical Center, Department of Intensive Care, Leiden, The Netherlands;Academic Medical Center, Department of Medical Informatics, Amsterdam, The Netherlands
Venue:
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Year:
2011

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Abstract

Recently, we devised a method to develop prognostic models incorporating patterns of sequential organ failure to predict the eventual hospital mortality at each day of intensive care stay. In this study, we aimed to understand, using a real world setting, how these models perform compared to physicians, who are exposed to additional information than the models. We found a slightly better discriminative ability for physicians (AUC range over days: 0.73-0.83 vs. 0.70-0.80) and a slightly better accuracy for the models (Brier score range: 0.14-0.19 vs. 0.16-0.19). However when we combined both sources of predictions we arrived at a significantly superior discrimination as well as accuracy (AUC range: 0.81-0.88; Brier score range: 0.11-0.15). Our results show that the models and the physicians draw on complementary information that can be best harnessed by combining both prediction sources. Extensive external validation and impact studies are imperative to further investigate the ability of the combined model.