Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model

Abstract

Rapid advances in ICT and collection of large amount of mobile health data are giving room to new ways of treating patients. Studies suggest that telemonitoring systems and predictive models for clinical support and patient empowerment may improve several pathologies, such as heart failure, which admissions rate is high. In the current medical practice, clinicians make use of simple rules that generate large number of false alerts. In order to reduce the false alerts, in this study, the predictive models to prevent decompensations that may lead into admissions are presented. They are based on mobile clinical data of 242 heart failure (HF) patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza). The best predictive model obtained is a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier using Bernoulli distribution. This predictive model performs with an AUC = 67% and reduces the false alerts per patient per year from 28.64 to 7.8. This way, the system predicts the risk of admission of ambulatory patients with higher reliability than current alerts.