• Cindy Crump, Sunil Saxena, Bruce Wilson, Patrick Farrell, Azhar Rafiq, and Christine Tsien Silvers.
• AFrame Digital, Inc, Reston, VA, USA.
• AMIA Annu Symp Proc. 2009 Jan 1;2009:124-8.

AbstractMultivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Models were validated on a reserved dataset from 16 additional patients. Receiver operating characteristic (ROC) curves were calculated. Area under the curve (AUC) was 91% for predicting improving outcome. The model's AUC for predicting declining outcome increased from 70% to 85% when the model was indexed to personalized baselines for each patient. The rule-based trending and alerting system was accurate 100% of the time in alerting a subsequent decline in condition. These techniques promise to improve the monitoring of ICU patients with high-sensitivity alerts, fewer false alarms, and earlier intervention.

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