SABIEN-ITACA Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain.
CLINTEC-Karolinska Institutet, 171 77 Solna, Sweden.
Sensors (Basel). 2020 Sep 17;20(18):5330. doi: 10.3390/s20185330.
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.
丰富的连续数据流可通过智能传感器获得，这为开发和分析医疗保健风险模型以及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以协助医疗专业人员以更精确和个性化的方式管理慢性病，结合智能传感器生成的数据，具有一定的市场需求。然而，当前对风险模型的理解依赖于健康变量或指标的静态快照，而不是考虑患者和疾病的变化和不同状态的持续和动态反馈循环。这项工作的基本原理是引入一种新的方法，基于健康传感器提供的患者动态行为，使用流程挖掘技术，为慢性病发现动态风险模型。结果表明，该方法具有可行性，已经针对高血压、肥胖症和糖尿病这三种慢性病，根据与代谢风险因素相关的动态行为，发现了三个动态模型。这些信息将支持医疗专业人员将目前一刀切的治疗和护理方法转变为个性化医疗策略，通过利用智能传感器生成的大量数据进行动态风险建模，为基于患者独特行为的治疗方法提供支持。
