Abstract
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.
Highlights
The arrival of a new generation of mobile personal technologies, medicine sensors, and wearable sensors, has skyrocketed the quantity of data available nowadays [1]
Using the Interactive Process Mining methodology, dynamic models associated with these chronic diseases were obtained as Interactive Process Indicators for the understanding, measurement, and optimisation of the processes associated with obesity, hypertension, hyperglycemia, allowing health professionals to navigate behind the models and to discover the specificity of the processes correlated with individuals
The hospital experts provided the data in several Comma-Separated Values (CSV) files, concretely one CSV file per table included in Table 2, where values were represented in a set of rows and columns
Summary
The arrival of a new generation of mobile personal technologies, medicine sensors, and wearable sensors, has skyrocketed the quantity of data available nowadays [1] This fact in combination with the massive introduction of Electronic Health Records (EHR) in medical systems has generated an enormous amount of information, the testimony of the patient’s passage along with the healthcare she/he receives. In this scenario, clinicians have the information collected within healthcare settings and data coming from multiple sources, such as personal and environmental data, thanks to wearable, sensors, Internet of Things (IoT), mobile applications, or even social media. Both definitions agreed in the fact of being of a long duration
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