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.

中文翻译：

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通过具有交互式过程挖掘的医疗传感器支持个性化慢性病管理的动态模型。

通过智能传感器可以获得丰富的连续数据流，这为开发和分析医疗保健风险模型并从数据中提取知识提供了独特的机会。开发新算法、可视化和决策支持工具有一个利基市场，可以帮助健康专业人员进行慢性病管理，以更精确和个性化的方式整合通过智能传感器生成的数据。然而，当前对风险模型的理解依赖于健康变量或措施的静态快照，而不是考虑到患者和疾病的变化和不同状态的持续和动态的行为反馈循环。这项工作的基本原理是引入一种新方法，利用过程挖掘技术，基于健康传感器提供的患者动态行为，发现慢性疾病的动态风险模型。结果表明了该方法的可行性，基于相关代谢危险因素的动态行为，已经发现了慢性疾病高血压、肥胖和糖尿病的三种动态模型。这些信息将支持卫生专业人员将当前一刀切的治疗和护理方法转化为个性化医疗策略，该策略适合基于患者独特行为的治疗，这得益于动态风险建模，利用了智能生成的大量数据。传感器。