Cardio-metabolic risk modeling and assessment through sensor-based measurements.
Int J Med Inform
; 165: 104823, 2022 09.
Article
in En
| MEDLINE
| ID: mdl-35763936
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cardiovascular Diseases
Type of study:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Int J Med Inform
Journal subject:
INFORMATICA MEDICA
Year:
2022
Document type:
Article