Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors

Abstract

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.

Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis

“…For instance, there are HRS designed to enable AA by counteracting elderly social isolation (e.g., by providing multi-modal coaching for social activities (Dimitrov et al, 2019 )), improving nutrition habits (e.g., providing tracking functionalities and “guiding” recommendations to observe a good macro-nutrients balance) (Espín et al, 2016 ; Calvaresi et al, 2021b ), promoting psycho-physical activity (e.g., proposing general-purpose and personalized physical tasks (Nassabi et al, 2014 )), including the cognitive activity (e.g., for equilibrium maintenance: HRS that provide reminders and personalized exercises to stimulate muscles and cognition skills (Calvaresi et al, 2021 )), and allowing tele-rehabilitation (e.g., HRS providing monitoring and guidance to recover a good range of motion, such as in the case of older individuals who have undergone lower-limbs surgery) (Buonocunto et al, 2018 ). Finally, several solutions have tried to provide longitudinal (multi-dimension) contributions, employing argumentation and rewarding mechanisms (Herpich et al, 2017 ), fighting addictions (e.g., smoking cessation) from both a social and physical health perspectives - by providing support in the monitoring and the craving phase (Calvaresi et al, 2019 ), and via automated (AI-based) and manual (formulated by doctors) recommendations and guidelines (e.g., for cancer survivors (Manzo et al, 2021 )).…”

“…Indeed, HRS are widely acknowledged as supporting the several dimensions of AA (Fernández-Ballesteros et al, 2013 ). The underlying contributions span over counteracting loneliness (Holwerda et al, 2012 ), promoting social support (Tomaka et al, 2006 ), enhancing social/family relationships (Victor et al, 2000 ), facilitating social activity and fostering a sense of personal fulfillment (Creecy et al, 1985 ), strengthening healthy habits (McPhee et al, 2004 )—such as smoking cessation and better nutrition (Pirlich & Lochs, 2001 ), including healthy active motor-habits (Cvecka et al, 2015 ), stimulating cognitive activities (Tardif & Simard, 2011 ), and treating/managing diseases and pathologies (e.g., diabetes and cancer) (Longo et al, 2010 ; Manzo et al, 2021 ). Indeed, AA goes beyond the health dimension of well-being in aging.…”