Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes

Abstract

Artificial intelligence techniques have been positioned in the resolution of problems in various areas of healthcare. Clinical decision support systems developed from this technology have optimized the healthcare of patients with chronic diseases through mobile applications. In this study, several models based on this methodology have been developed to calculate the basal insulin dose in patients with type I diabetes using subcutaneous insulin infusion pumps. Methods. A pilot experimental study was performed with data from 56 patients with type 1 diabetes who used insulin infusion pumps and underwent continuous glucose monitoring. Several models based on artificial intelligence techniques were developed to analyze glycemic patterns based on continuous glucose monitoring and clinical variables in order to estimate the basal insulin dose. We used neural networks (NNs), Bayesian networks (BNs), support vector machines (SVMs), and random forests (RF). We then evaluated the agreement between predicted and actual values using several statistical error measurements: mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R), and determination coefficient (R2). Results. Twenty-four different models were obtained, one for each hour of the day, with each chosen technique. Correlation coefficients obtained with RF, SVMs, NNs, and BNs were 0.9999, 0.9921, 0.0303, and 0.7754, respectively. The error increased between 06:00 and 07:00 and between 13:00 and 17:00. Conclusions. The performance of the RF technique was excellent and got very close to the actual values. Intelligence techniques could be used to predict basal insulin dose. However, it is necessary to explore the validity of the results and select the target population. Models that allow for more accurate levels of prediction should be further explored.

Potential next-generation medications for self-administered platforms

“…Daily adjustments in insulin supplementation are challenging, but dosages were traditionally established following simple rules based on empiricism or experienced clinicians' suggestions. Preset and advised drug administration frequently results in more adverse events if a healthcare system is inaccessible [ 3 ]. Self-regulated drug delivery devices with closed-loop systems are designed to achieve autonomous delivery of therapeutics and improve patient compliance.…”

“…Machine learning offers a promising way to reform medical diagnosis and therapy by providing accurate and individualized analysis and forecasts based on a vast amount of patient data [ 12 ]. With continuous monitoring, even a small number of patients can generate a vast amount of data for analysis, which can further facilitate machine learning [ 3 ]. Given the advances in nanobiotechnology, more biocompatible nanodevices might be introduced in the future.…”

“…With a preset limitation of maximal analgesic dosage and releasing intervals, patients gain more satisfaction and quality of life via extracorporeal patient-controlled analgesics devices [ 18 ]. Meanwhile, as devices are put in place and used more and more frequently, more data are generated and collected [ 3 ]. This information can be de-identified and transmitted through the internet or cloud systems to the healthcare providers or pharmaceutical researchers.…”

“…Daily adjustments in insulin supplementation are challenging, but dosages were traditionally established following simple rules based on empiricism or experienced clinicians' suggestions. Preset and advised drug administration frequently results in more adverse events if a healthcare system is inaccessible [ 3 ].…”

“…In the United States, a developed country, these expenditures gradually increase, accounting for 90% of all annual healthcare spending in the US [ 2 ]. In developing countries, on the other hand, the scattered healthcare system hindered accessibility to clinical consultation, and poorly controlled chronic diseases resulted in numerous complications [ 3 ].…”