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Data-driven vs knowledge-driven inference of health outcomes in the ageing population: A case study

Lookup NU author(s): Professor Paolo MissierORCiD


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© 2020 Copyright for this paper by its author(s).Preventive, Predictive, Personalised and Participative (P4) medicine has the potential to not only vastly improve people's quality of life, but also to significantly reduce healthcare costs and improve its efficiency. Our research focuses on age-related diseases and explores the opportunities offered by a data-driven approach to predict wellness states of ageing individuals, in contrast to the commonly adopted knowledge-driven approach that relies on easy-to-interpret metrics manually introduced by clinical experts. This is done by means of machine learning models applied on the My Smart Age with HIV (MySAwH) dataset, which is collected through a relatively new approach especially for older HIV patient cohorts. This includes Patient Related Outcomes values from mobile smartphone apps and activity traces from commercial-grade activity loggers. Our results show better predictive performance for the data-driven approach. We also show that a post hoc interpretation method applied to the predictive models can provide intelligible explanations that enable new forms of personalised and preventive medicine.

Publication metadata

Author(s): Ferrari D, Guaraldi G, Mandreoli F, Martoglia R, Milic J, Missier P

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: CEUR Workshop Proceedings

Year of Conference: 2020

Online publication date: 30/03/2020

Acceptance date: 02/04/2016

Publisher: CEUR-WS