Introduction: Diagnosis of T2DM necessitates clinical tests that are time-consuming and expensive. Machine learning (ML) techniques can accelerate the diagnosis and classification of T2DM and allow clinicians to personalize treatments based on blood glucose concentrations (BGC) , physical fitness (PF) , and diabetes distress patterns observed in daily life. Analyzing electronic health records (EHR) , physiological variables collected with wearable devices, and patient-reported outcomes (PROs) using ML techniques can lead to the development of clinical decision support tools that provide a comprehensive picture of an individual’s diabetes management needs.

Methods: Clinical experimental data (n=85, F:40/M:45, HbA1c: 7.83±2.16; age: 57±7.72: BMI:±6.72; means and SDs) were used to identify clusters of subjects based on medical tests, and ML models were developed using readily measured data to classify new subjects to the identified clusters. Latent variable methods and k-means clustering were used to identify clusters based on HbA1c, physical performance tests, and PROs. ML models, including logistic regression (LR) and support vector machines (SVM) , were developed to assign new subjects to the identified clusters using the readily measured input variables from wearable devices, EHR and PROs.

Results: Three distinct subject clusters were identified within the study cohort based on the descriptive variables. New subjects were assigned to the identified clusters with 87% and 91% accuracy for LR and SVM, respectively.

Conclusion: EHRs, wearable device data, and PROs can be used to accurately and conveniently identify a person’s overall BGC, PF, and diabetes distress to aid in clinical decision-making. In the future, clinical decision support tools can be developed for personalized treatment suggestions based on cluster membership.


A.Shahidehpour: None. C.Fritschi: None. M.Rashid: None. A.Cinar: None. L.T.Quinn: n/a.

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