Cardio-metabolic risk modeling and assessment through sensor-based measurements. (September 2022)

• Record Type: Journal Article
• Authors: Giorgi, Daniela; Bastiani, Luca; Morales, Maria Aurora; Pascali, Maria Antonietta; Colantonio, Sara; Coppini, Giuseppe
• Is Part Of: International journal of medical informatics. Volume 165(2022)
• Publication Date: 2022-09
• Subjects: Cardio-metabolic risk -- Risk modeling -- Self-monitoring -- Smart mirror -- Sensor-based measurements -- Structural Equation Modeling -- Self Organizing Maps; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2022.104823
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Smart devices open new ways to assess cardiometabolic risk in daily life. We define a model based on anthropometry, glycolipid factors, and vascular function. Measurements of model variables are taken on a subject face using the Wize Mirror system. SOM networks can detect subjects at-risk in good agreement with clinical evaluation. Abstract: Objective: Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors. Methods: We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables. Results: Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4 %, sensitivity 82.5 %, and specificity 82.1 % . Conclusions: Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.