Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology.
Annu Int Conf IEEE Eng Med Biol Soc
; 2018: 4456-4459, 2018 Jul.
Article
en En
| MEDLINE
| ID: mdl-30441340
ABSTRACT
Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Estado Prediabético
/
Sistemas de Apoyo a Decisiones Clínicas
/
Diabetes Mellitus Tipo 2
/
Dispositivos Electrónicos Vestibles
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2018
Tipo del documento:
Article