Abstract
Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.
Highlights
The importance of sustained support over extended periods of time is important for patients, especially for rehabilitation, chronic diseases, or other conditions such as those affecting cancer survivors
The usage of data analytics based on machine learning (ML) techniques applied to this vast body of data can provide a number of features including: patient stratification, identification of unusual behavior patterns, prediction of wellness and distress parameters, assessment of home exercise performance, improvement of adherence to treatment, identification and prevention of risk situations
The information contained in these trajectories requires managing and integrating very diverse types of data, ranging from electronic health records [8, 18] to self-reported observations [20] or sensor measurements recorded by a wearable device [10]
Summary
The importance of sustained support over extended periods of time is important for patients, especially for rehabilitation, chronic diseases, or other conditions such as those affecting cancer survivors. In these situations, patients are often left at home, expected to continue their lives and activities, while dealing with potential complications and issues inherent to their health conditions [27]. The information contained in these trajectories requires managing and integrating (potentially) very diverse types of data, ranging from electronic health records [8, 18] to self-reported observations [20] or sensor measurements recorded by a wearable device [10]. Agent-based systems can ensure a high-degree of personalization [4], autonomy, distributed collaborative/competitive intelligence, and security
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