Abstract
E-health sustainable systems can be optimized by empowering patients in self-care programs through artificial intelligence ecosystems in which both doctors and patients interact in an agile way. This work proposes agent-based simulators as a mechanism for predicting the repercussions of certain self-care programs in certain patients for finding the most appropriate ones. In order to make this easy for both doctors and patients, mobile agents are used to configure an app for each patient, and this app provides the resources to each self-care program. Mobile agents include a machine-learning module for learning which programs are the most appropriate for each patient. This approach is illustrated with two agent-based simulators for respectively reducing negative emotions such as depression and controlling heart rate variability extreme values related to stress. The resulting app was evaluated with a group of users with the Usefulness, Satisfaction and Ease of use (USE) scale and obtained 73% in usefulness, 77% in satisfaction, and 68% in ease of use. This trial is registered with According to the recommendations of the International Committee of Medical Journal Editors (ICMJE), this manuscript states that all experiments have been approved with the ethical committee CEICA from Community of Aragon (Spain) with registration number C.I.PI18/099.
Highlights
E-health sustainable systems can be supported by providing tools to instruct patients in conducting self-care programs [1]
Self-care programs can help in motor rehabilitation [2], as one can observe in the effects of the studies with augmented-reality and virtual-reality applications and serious games for this purpose
Self-care programs can be especially useful for patients with chronic conditions for achieving sustainable primary care [5]
Summary
E-health sustainable systems can be supported by providing tools to instruct patients in conducting self-care programs [1]. We developed and validated an ABS of emotions in mindfulness programs (ABSEM) [9] and an ABS of the influence of mindfulness programs on heart rate variability (HRV) called ABS-MindHeart [10] These systems lacked the proper mechanism for transferring self-care programs into an app that could guide patients. It lacked the proper machine-learning (ML) mechanism for improving the initial configuration of these programs and the supervised learning for adapting to users’ needs based on their feedback In this context, this work proposes the usage of ABSs in combination with ML for providing a customized experience for patients extracting knowledge from doctors or instructors through mobile agents.
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