{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:20:55Z","timestamp":1772724055650,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T00:00:00Z","timestamp":1600300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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\u2019 unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.<\/jats:p>","DOI":"10.3390\/s20185330","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T07:27:33Z","timestamp":1600414053000},"page":"5330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4903-3896","authenticated-orcid":false,"given":"Zoe","family":"Valero-Ramon","sequence":"first","affiliation":[{"name":"SABIEN-ITACA Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera S\/N, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2819-5597","authenticated-orcid":false,"given":"Carlos","family":"Fernandez-Llatas","sequence":"additional","affiliation":[{"name":"SABIEN-ITACA Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera S\/N, 46022 Valencia, Spain"},{"name":"CLINTEC-Karolinska Institutet, 171 77 Solna, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernardo","family":"Valdivieso","sequence":"additional","affiliation":[{"name":"Hospital Universitario y Politecnico La Fe, 46026 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1806-8575","authenticated-orcid":false,"given":"Vicente","family":"Traver","sequence":"additional","affiliation":[{"name":"SABIEN-ITACA Universitat Polit\u00e8cnica de Val\u00e8ncia, Camino de Vera S\/N, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","article-title":"Big data: A survey","volume":"19","author":"Chen","year":"2014","journal-title":"Mob. Netw. Appl."},{"key":"ref_2","unstructured":"World Health Organization (2020). Noncommunicable Diseases, WHO."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bernstein, A.B. (2004). Health Care in America: Trends in Utilization.","DOI":"10.1037\/e309002005-001"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/s10654-017-0315-2","article-title":"Chronic disease research in Europe and the need for integrated population cohorts","volume":"32","author":"Brennan","year":"2017","journal-title":"Eur. J. Epidemiol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Raghupathi, W., and Raghupathi, V. (2018). An empirical study of chronic diseases in the United States: A visual analytics approach to public health. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15030431"},{"key":"ref_6","first-page":"35","article-title":"Caring for people with multiple Chronic conditions in Europe","volume":"20","author":"Struckmann","year":"2014","journal-title":"EuroHealth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0140-6736(16)31679-8","article-title":"Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990\u20132015: A systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"Collaborators","year":"2016","journal-title":"Lancet"},{"key":"ref_8","unstructured":"World Health Organization (2020). Hypertension, WHO."},{"key":"ref_9","unstructured":"Joint National Committee on Detection, Treatment of High Blood Pressure, National High Blood Pressure Education Program, and Coordinating Committee (1995). Report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure."},{"key":"ref_10","unstructured":"World Health Organization (2020). Obesity and Overweight, WHO."},{"key":"ref_11","unstructured":"World Health Organization (2020). Diabetes, WHO."},{"key":"ref_12","unstructured":"World Health Organization (2006). Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycaemia: Report of a WHO\/IDF Consultation, World Health Organization."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.procs.2016.04.103","article-title":"Patient monitoring system based on internet of things","volume":"83","author":"Oviedo","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Geman, O., and Chiuchisan, I. (2019, January 29\u201330). Monitoring Healthcare System for Patients with Chronic Diseases based on the Sensors Network and Internet of Things. Proceedings of the IEEE 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI), Lisbon, Portugal.","DOI":"10.1109\/ISSI47111.2019.9043748"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.nbt.2012.03.009","article-title":"The future of technologies for personalised medicine","volume":"29","author":"Harvey","year":"2012","journal-title":"New Biotechnol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1097\/01.ogx.0000472121.21647.38","article-title":"Precision medicine\u2014Personalized, problematic, and promising","volume":"70","author":"Jameson","year":"2015","journal-title":"Obstet. Gynecol. Surv."},{"key":"ref_17","unstructured":"Porter, M.E., and Teisberg, E.O. (2006). Redefining Health Care: Creating Value-Based Competition on Results, Harvard Business Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1056\/NEJMp1500523","article-title":"A New Initiative on Precision Medicine","volume":"372","author":"Collins","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1017\/cts.2018.31","article-title":"Realizing the full potential of precision health: The need to include patient-reported health behavior, mental health, social determinants, and patient preferences data","volume":"2","author":"Glasgow","year":"2018","journal-title":"J. Clin. Transl. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1002\/sim.3991","article-title":"Evaluating health risk models","volume":"29","author":"Whittemore","year":"2010","journal-title":"Stat. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e1921213","DOI":"10.1001\/jamanetworkopen.2019.21213","article-title":"Association of Time-Varying Blood Pressure With Chronic Kidney Disease Progression in Children","volume":"3","author":"Reynolds","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1136\/bmj.316.7125.133","article-title":"Integrated care pathways","volume":"316","author":"Campbell","year":"1998","journal-title":"BMJ"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Schienkiewitz, A., Mensink, G.B., and Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18\u201379 years. BMC Public Health, 12.","DOI":"10.1186\/1471-2458-12-658"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1001\/jama.282.16.1523","article-title":"The disease burden associated with overweight and obesity","volume":"282","author":"Must","year":"1999","journal-title":"JAMA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1161\/CIRCOUTCOMES.115.002127","article-title":"Gender-related differential effects of obesity on health-related quality of life via obesity-related Comorbidities: A mediation analysis of a French Nationwide survey","volume":"9","author":"Audureau","year":"2016","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"235","DOI":"10.2337\/diab.41.2.235","article-title":"Duration of obesity increases the incidence of NIDDM","volume":"41","author":"Everhart","year":"1992","journal-title":"Diabetes"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1136\/jech.2003.015651","article-title":"Overweight and obesity and weight change in middle aged men: Impact on cardiovascular disease and diabetes","volume":"59","author":"Wannamethee","year":"2005","journal-title":"J. Epidemiol. Community Health"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1370\/afm.2169","article-title":"Perspectives in Primary Care: Knowing the Patient as a Person in the Precision Medicine Era","volume":"16","author":"Ziegelstein","year":"2018","journal-title":"Ann. Fam. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1605271","DOI":"10.1002\/adfm.201605271","article-title":"Wearable and miniaturized sensor technologies for personalized and preventive medicine","volume":"27","author":"Tricoli","year":"2017","journal-title":"Adv. Funct. Mater."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Saponara, S., Donati, M., Fanucci, L., and Celli, A. (2016). An Embedded sensing and communication platform, and a healthcare model for remote monitoring of chronic diseases. Electronics, 5.","DOI":"10.3390\/electronics5030047"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Mirza, F., Naeem, M.A., and Nguyen, M. (2017, January 26\u201328). A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback. Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), Wellington, New Zealand.","DOI":"10.1109\/CSCWD.2017.8066704"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jbi.2017.12.015","article-title":"Discovering role interaction models in the Emergency Room using Process Mining","volume":"78","author":"Alvarez","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15434","DOI":"10.3390\/s131115434","article-title":"Process mining for individualized behavior modeling using wireless tracking in nursing homes","volume":"13","author":"Benedi","year":"2013","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0004-3702(96)00025-2","article-title":"A framework for knowledge-based temporal abstraction","volume":"90","author":"Shahar","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_35","unstructured":"Concaro, S., Sacchi, L., Cerra, C., Stefanelli, M., Fratino, P., and Bellazzi, R. (2009, January 14\u201318). Temporal data mining for the assessment of the costs related to diabetes mellitus pharmacological treatment. Proceedings of the AMIA Annual Symposium Proceedings. American Medical Informatics Association, San Francisco, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/JBHI.2015.2420534","article-title":"DBN-extended: A dynamic Bayesian network model extended with temporal abstractions for coronary heart disease prognosis","volume":"20","author":"Orphanou","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_37","first-page":"56","article-title":"Applying temporal abstraction in medical information systems","volume":"1","author":"Balaban","year":"2003","journal-title":"Ann. Math. Comput. Teleinform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fernandez-Llatas, C., Sacchi, L., Benedi, J.M., Dagliati, A., Traver, V., and Bellazzi, R. (2014, January 1\u20134). Temporal abstractions to enrich activity-based process mining corpus with clinical time series. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864481"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s13142-015-0324-1","article-title":"Building new computational models to support health behavior change and maintenance: New opportunities in behavioral research","volume":"5","author":"Hekler","year":"2015","journal-title":"Transl. Behav. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Van Der Aalst, W. (2016). Process Mining. Data Science in Action, Springer.","DOI":"10.1007\/978-3-662-49851-4"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fernandez-Llatas, C., Bayo, J.L., Martinez-Romero, A., Bened\u00ed, J.M., and Traver, V. (2016, January 24\u201327). Interactive pattern recognition in cardiovascular disease management. A process mining approach. Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2016.7455906"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Van der Aalst, W. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer.","DOI":"10.1007\/978-3-642-19345-3"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.jbi.2016.04.007","article-title":"Process mining in healthcare: A literature review","volume":"61","author":"Rojas","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijmedinf.2015.12.018","article-title":"Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital","volume":"88","author":"Yoo","year":"2016","journal-title":"Int. J. Med. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mans, R.S., Schonenberg, M., Song, M., van der Aalst, W.M., and Bakker, P.J. (2008). Application of process mining in healthcare\u2014A case study in a dutch hospital. International Joint Conference on Biomedical Engineering Systems and Technologies, Springer.","DOI":"10.1007\/978-3-540-92219-3_32"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ibanez-Sanchez, G., Fernandez-Llatas, C., Martinez-Millana, A., Celda, A., Mandingorra, J., Aparici-Tortajada, L., Valero-Ramon, Z., Munoz-Gama, J., Sep\u00falveda, M., and Rojas, E. (2019). Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16101783"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1001\/jama.2016.3867","article-title":"Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System: A New Model for Biomedical Research","volume":"315","author":"Chambers","year":"2016","journal-title":"JAMA"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cameranesi, M., Diamantini, C., Mircoli, A., Potena, D., and Storti, E. (2020). Extraction of User Daily Behavior from Home Sensors through Process Discovery. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2020.2990537"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Valero-Ramon, Z., Fernandez-Llatas, C., Martinez-Millana, A., and Traver, V. (2019, January 5\u20137). A dynamic behavioral approach to nutritional assessment using process mining. Proceedings of the 32nd IEEE International Symposium on Computer-Based Medical Systems, Cordoba, Spain.","DOI":"10.1109\/CBMS.2019.00085"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5671","DOI":"10.3390\/ijerph10115671","article-title":"Applying evidence-based medicine in telehealth: An interactive pattern recognition approximation","volume":"10","author":"Meneu","year":"2013","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fernandez-Llatas, C. (2020). Interactive Process Mining in Healthcare, Springer.","DOI":"10.1007\/978-3-030-53993-1"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Llatas, C., and Garc\u00eda-G\u00f3mez, J.M. (2015). Using Process Mining for Automatic Support of Clinical Pathways Design. Data Mining in Clinical Medicine, Springer. Number 1246 in Methods in Molecular Biology.","DOI":"10.1007\/978-1-4939-1985-7_5"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e127","DOI":"10.2196\/jmir.8884","article-title":"Multidisciplinary Collaboration in the Treatment of Patients with Type 2 Diabetes in Primary Care: Analysis Using Process Mining","volume":"20","author":"Conca","year":"2018","journal-title":"J. Med Internet Res."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Valero-Ramon, Z., Fernandez-Llatas, C., Martinez-Millana, A., and Traver, V. (2020). Interactive Process Indicators for Obesity Modelling Using Process Mining. Advanced Computational Intelligence in Healthcare-7, Springer.","DOI":"10.1007\/978-3-662-61114-2_4"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Song, M., G\u00fcnther, C.W., and Van der Aalst, W.M. (2008). Trace clustering in process mining. International Conference on Business Process Management, Springer.","DOI":"10.1007\/978-3-642-00328-8_11"},{"key":"ref_56","unstructured":"American Heart Association (2020). Understanding Blood Pressure Readings, AHA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S2213-8587(16)30328-X","article-title":"The need for international consensus on prediabetes","volume":"5","author":"Makaroff","year":"2017","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Danalis, A., McCurdy, C., and Vetter, J.S. (2012, January 21\u201325). Efficient quality threshold clustering for parallel architectures. Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, Shanghai, China.","DOI":"10.1109\/IPDPS.2012.99"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e012493","DOI":"10.1136\/bmjopen-2016-012493","article-title":"Retrospective observational study of emergency admission, readmission and the \u2018weekend effect\u2019","volume":"7","author":"Shiue","year":"2017","journal-title":"BMJ Open"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5330\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:00Z","timestamp":1760177460000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,17]]},"references-count":59,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185330"],"URL":"https:\/\/doi.org\/10.3390\/s20185330","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,17]]}}}