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

[EN] 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.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. 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Dynamic Risk Models for Characterising Chronic Diseases' Behaviour Using Process Mining Techniques

[ES] Los modelos de riesgo en el ámbito de la salud son métodos estadísticos que brindan advertencias tempranas sobre el riesgo de una persona de sufrir un episodio adverso en el futuro. Por lo general, utilizan la información almacenada de forma rutinaria en los sistemas de información hospitalaria para ofrecer una probabilidad individual de desarrollar un resultado negativo futuro en un período determinado. Concretamente, en el campo de las enfermedades crónicas que comparten factores de riesgo comunes, los modelos de riesgo se basan en el análisis de esos factores de riesgo -tensión arterial elevada, glucemia elevada, lípidos sanguíneos anormales, sobrepeso y obesidad- y sus medidas biométricas asociadas. Estas medidas se recopilan durante la práctica clínica de manera periódica y, se incorporan a los modelos de riesgo para apoyar a los médicos en la toma de decisiones. Para crear modelos de riesgo que incluyan la variable temporal, se podrían utilizar técnicas basadas en datos (Data-Driven), de forma que se tuviera en cuenta el historial de los pacientes almacenado en los registros médicos electrónicos, extrayendo conocimiento de los datos en bruto. Sin embargo, en el ámbito de la salud, los resultados de la minería de datos suelen ser percibidos por los expertos en salud como cajas negras y, en consecuencia, no confían en sus decisiones. El paradigma Interactivo permite a los expertos comprender los resultados, para que los profesionales puedan corregir esos modelos de acuerdo con su conocimiento y experiencia, proporcionando modelos perceptivos y cognitivos. En este contexto, la minería de procesos es una técnica de minería de datos que permite la implementación del paradigma Interactivo, ofreciendo una comprensión clara del proceso de atención y proporcionando modelos comprensibles para el ser humano. Las condiciones crónicas generalmente se describen mediante imágenes estáticas de variables, como factores genéticos, fisiológicos, ambientales y de comportamiento. Sin embargo, la perspectiva dinámica, temporal y de comportamiento no se consideran comúnmente en los modelos de riesgo. Eso significa que el último estado de riesgo se convierte en el estado real del paciente. No obstante, la condición de los pacientes podría verse influenciada por sus condiciones dinámicas pasadas. El objetivo de esta tesis es proporcionar una visión novedosa del riesgo asociado a un paciente, basada en tecnologías Data-Driven que ofrezcan una visión dinámica de su evolución con respecto a su condición crónica. Técnicamente, supone abordar los modelos de riesgo incorporando la perspectiva dinámica y comportamental de los pacientes gracias a la información incluida en la Historia Clínica Electrónica. Los resultados obtenidos a lo largo de esta tesis muestran cómo las tecnologías de minería de procesos pueden aportar una visión dinámica e interactiva de los modelos de riesgo de enfermedades crónicas. Estos resultados pueden ayudar a los profesionales de la salud en la práctica diaria para una mejor comprensión del estado de salud de los pacientes y una mejor clasificación de su estado de riesgo.[CA] Els models de risc en l'àmbit de la salut són mètodes estadístics que brinden advertències primerenques sobre el risc d'una persona de patir un episodi advers en el futur. Generalment, utilitzen la informació emmagatzemada de forma rutinària en els sistemes d'informació hospitalària per a oferir una probabilitat individual de desenrotllar un resultat negatiu futur en un període determinat. Concretament, en el camp de les malalties cròniques que compartixen factors de risc comú, els models de risc es basen en l'anàlisi d'eixos factors de risc -tensió arterial elevada, glucèmia elevada, lípids sanguinis anormals, sobrecàrrega i obesitat- i les seues mesures biomètriques associades. Estes mesures es recopilen durant la pràctica clínica ben sovint de manera periòdica i, en conseqüència, s'incorporen als models de risc i recolzen la presa de decisions dels metges. Per a crear estos models de risc que incloguen la variable temporal es podrien utilitzar tècniques basades en dades (Data-Driven) , de manera que es tinguera en compte l'historial dels pacients disponible en els registres mèdics electrònics, extraient coneixement de les dades en brut. No obstant això, en l'àmbit de la salut, els resultats de la mineria de dades solen ser percebuts pels experts en salut com a caixes negres i, en conseqüència, no confien en les decisions dels algoritmes. El paradigma Interactiu permet als experts comprendre els resultats, perquè els professionals puguen corregir eixos models d'acord amb el seu coneixement i experiència, proporcionant models perceptius i cognitius. En este context, la mineria de processos és una tècnica de mineria de dades que permet la implementació del paradigma Interactiu, oferint una comprensió clara del procés d'atenció i proporcionant models comprensibles per al ser humà. Les condicions cròniques generalment es descriuen per mitjà d'imatges estàtiques de variables, com a factors genètics, fisiològics, ambientals i de comportament. No obstant això, la perspectiva dinàmica, temporal i de comportament no es consideren comunament en els models de risc. Això significa que l'últim estat de risc es convertix en l'estat real del pacient. No obstant això, la condició dels pacients podria veure's influenciada per les seues condicions dinàmiques passades. L'objectiu d'esta tesi és proporcionar una visió nova del risc, associat a un pacient, basada en tecnologies Data-Driven que oferisquen una visió dinàmica de l'evo\-lució dels pacients respecte a la seua condició crònica. Tècnicament, suposa abordar els models de risc incorporant la perspectiva dinàmica i el comportament dels pacients als models de risc gràcies a la informació inclosa en la Història Clínica Electrònica. Els resultats obtinguts al llarg d'esta tesi mostren com les tecnologies de mineria de processos poden aportar una visió dinàmica i interactiva dels models de risc de malalties cròniques. Estos resultats poden ajudar els professionals de la salut en la pràctica diària per a una millor comprensió de l'estat de salut dels pacients i una millor classificació del seu estat de risc.[EN] Risk models in the healthcare domain are statistical methods that provide early warnings about a person's risk for an adverse episode in the future. They usually use the information routinely stored in Hospital Information Systems to offer an individual probability for developing a future negative outcome in a given period. Concretely, in the field of chronic diseases that share common risk factors, risk models are based on the analysis of those risk factors -raised blood pressure, raised glucose levels, abnormal blood lipids, and overweight and obesity- and their associated biometric measures. These measures are collected during clinical practice frequently in a periodic manner, and accordingly, they are incorporated into the risk models to support clinicians' decision-making. Data-Driven techniques could be used to create these temporal-aware risk models, considering the patients' history included in Electronic Health Records, and extracting knowledge from raw data. However, in the healthcare domain, Data Mining results are usually perceived by the health experts as black-boxes, and in consequence, they do not trust in the algorithms' decisions. The Interactive paradigm allows experts to understand the results, in that sense, professionals can correct those models according to their knowledge and experience, providing perceptual and cognitive models. In this context, Process Mining is a Data Mining technique that enables the implementation of the Interactive paradigm, offering a clear care process understanding and providing human-understandable models. Chronic conditions are usually described by static pictures of variables, such as genetic, physiological, environmental, and behavioural factors. Nevertheless, the dynamic, temporal, and behavioural perspectives are not commonly considered in the risk models. That means the last status of the risk becomes the actual status of the patient. However, the patients' condition could be influenced by their past dynamic circumstances. The objective of this thesis is to provide a novel risk vision based on Data-Driven technologies offering a dynamic view of the patients' evolution regarding their chro\-nic condition. Technically, it supposes to approach risk models incorporating the dynamic and behavioural perspective of patients to the risk models thanks to the information included in the Electronic Health Records. The results obtained throughout this thesis show how Process Mining technologies can bring a dynamic and interactive view of chronic disease risk models. These results can support health professionals in daily practice for a better understanding of the patients' health condition and a better classification of their risk status.Valero Ramón, Z. (2022). Dynamic Risk Models for Characterising Chronic Diseases' Behaviour Using Process Mining Techniques [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181652TESI

Meaningful Big Data Integration For a Global COVID-19 Strategy

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Social media analytics: a survey of techniques, tools and platforms

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