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.

Data-driven vs knowledge-driven inference of health outcomes in the ageing population: A case study / Ferrari, D.; Guaraldi, G.; Mandreoli, F.; Martoglia, R.; Milić, Jovana; Missier, Paolo. - 2578:(2020). (Intervento presentato al convegno Workshops of the 23rd International Conference on Extending Database Technology/23rd International Conference on Database Theory, EDBT-ICDT-WS 2020 tenutosi a Copenaghen, Danimarca nel 2020).

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

Ferrari D.;Guaraldi G.;Mandreoli F.;Martoglia R.;Milić Jovana;
2020

Abstract

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.
2020
Workshops of the 23rd International Conference on Extending Database Technology/23rd International Conference on Database Theory, EDBT-ICDT-WS 2020
Copenaghen, Danimarca
2020
2578
Ferrari, D.; Guaraldi, G.; Mandreoli, F.; Martoglia, R.; Milić, Jovana; Missier, Paolo
Data-driven vs knowledge-driven inference of health outcomes in the ageing population: A case study / Ferrari, D.; Guaraldi, G.; Mandreoli, F.; Martoglia, R.; Milić, Jovana; Missier, Paolo. - 2578:(2020). (Intervento presentato al convegno Workshops of the 23rd International Conference on Extending Database Technology/23rd International Conference on Database Theory, EDBT-ICDT-WS 2020 tenutosi a Copenaghen, Danimarca nel 2020).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1204925
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