Cargando…

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health profes...

Descripción completa

Detalles Bibliográficos
Autores principales: Valero-Ramon, Zoe, Fernandez-Llatas, Carlos, Valdivieso, Bernardo, Traver, Vicente
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570892/
https://www.ncbi.nlm.nih.gov/pubmed/32957673
http://dx.doi.org/10.3390/s20185330
_version_ 1783597051949350912
author Valero-Ramon, Zoe
Fernandez-Llatas, Carlos
Valdivieso, Bernardo
Traver, Vicente
author_facet Valero-Ramon, Zoe
Fernandez-Llatas, Carlos
Valdivieso, Bernardo
Traver, Vicente
author_sort Valero-Ramon, Zoe
collection PubMed
description Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients’ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients’ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.
format Online
Article
Text
id pubmed-7570892
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75708922020-10-28 Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining Valero-Ramon, Zoe Fernandez-Llatas, Carlos Valdivieso, Bernardo Traver, Vicente Sensors (Basel) Article Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients’ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients’ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors. MDPI 2020-09-17 /pmc/articles/PMC7570892/ /pubmed/32957673 http://dx.doi.org/10.3390/s20185330 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Valero-Ramon, Zoe
Fernandez-Llatas, Carlos
Valdivieso, Bernardo
Traver, Vicente
Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title_full Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title_fullStr Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title_full_unstemmed Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title_short Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining
title_sort dynamic models supporting personalised chronic disease management through healthcare sensors with interactive process mining
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570892/
https://www.ncbi.nlm.nih.gov/pubmed/32957673
http://dx.doi.org/10.3390/s20185330
work_keys_str_mv AT valeroramonzoe dynamicmodelssupportingpersonalisedchronicdiseasemanagementthroughhealthcaresensorswithinteractiveprocessmining
AT fernandezllatascarlos dynamicmodelssupportingpersonalisedchronicdiseasemanagementthroughhealthcaresensorswithinteractiveprocessmining
AT valdiviesobernardo dynamicmodelssupportingpersonalisedchronicdiseasemanagementthroughhealthcaresensorswithinteractiveprocessmining
AT travervicente dynamicmodelssupportingpersonalisedchronicdiseasemanagementthroughhealthcaresensorswithinteractiveprocessmining