Near-optimal insulin treatment for diabetes patients: A machine learning approach.

Blood glycemic control is crucial for minimizing severe side effects in diabetes mellitus. Currently, two opposing treatment approaches exist: in formulaic methods, insulin care is calculated by parameter-based computation (i.e., correction factor, insulin-to-carb ratio, and absorption duration), which are fixed by the medical team based on the history of a tested patient blood glucose levels (BGLs). Alternatively, closed-loop methods test glycemic level via sensors and provide insulin boluses based on sensor data thus ignoring other medical information. Unlike the body, both these systems are reactive - chasing insulin dosage based on fluctuating BGL - resulting in significant fluctuations of glucose values, rather than the relatively flat profile normal to the body's glycemic control. Extended periods of these fluctuations - particularly high BGLs (hyperglycemia) result in vascular and organ epithelial damage, which increases comorbidities and is ultimately life-threatening. We propose an individualized treatment scheme based on machine learning artificial intelligence, which combines the best of both approaches and is tailored to the individual. We model patient reaction to insulin treatment as Markov decision process (MDP) thus allowing the system to find a unique, individualized and dynamically updating insulin care policy that would lead to flat blood glucose profiles in target areas. We incorporate an individualized "health reward function", preferably from the medical team, describing a grading scheme of BGL tailored to the patient for even more precise glycemic control. The solution to MDP is found via reinforcement learning, which yields an individualized, optimal insulin care policy. This policy can prevent hypoglycemia, minimize high glucose duration and glycemic fluctuations. It can be further updated as the patient undergoes environmental changes. Significantly, our method provides the care team a constantly updated patient model, allowing them to better understand and support the patient.