Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico.
Yue YouSvetlana V DoubovaDiana Pinto-MasisRicardo Pérez-CuevasVíctor Hugo Borja-AburtoAlan HubbardPublished in: BMC medical informatics and decision making (2019)
DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.
Keyphrases
- machine learning
- glycemic control
- quality improvement
- healthcare
- type diabetes
- end stage renal disease
- big data
- newly diagnosed
- ejection fraction
- artificial intelligence
- chronic kidney disease
- primary care
- prognostic factors
- blood glucose
- deep learning
- metabolic syndrome
- electronic health record
- insulin resistance
- risk assessment
- decision making
- cross sectional
- skeletal muscle
- adipose tissue