Data collection - Simulated data has limited features and questionable fit to real

1. Machine Learning Based Algorithm for Automatic Insulin Regulation in Insulin PumpsFinal project submitted by Talia Melamed1 and Einav Yemini1 under the supervision of Dr. Ofer Yodfat2, Mr. Iddo Michael Gescheit2(1)Tel Aviv University (2) Medingo Ltd. 4. Results 1. Introduction • Feature Analysis • Weight analysis in perceptron - sign of each input parameter: Expected correlation • Type 1 Diabetes Mellitus is a chronic metabolic disease caused by insufficient secretion of insulin by the beta cells in the pancreas, resulting in an inability to control blood glucose concentrations. Type 1 diabetes patients must have constant and tight supervision of their blood glucose levels via multiple blood glucose measurements and insulin administration to avoid short and long-term complications. • Insulin administration is typically carried out by Multiple Daily Insulin Injections (MDII) or Continuous Subcutaneous • Insulin Infusion (CSII), i.e., insulin pump therapy. An insulin pump is a processor-controlled medical device that • continuously delivers insulin to the subcutaneous tissue through a soft cannula replaced every 2-3 days. • Further development in Continuous Glucose Monitoring (CGM) technology offers the patients tighter control over BG levels. • Recent academic work has been researching the implementation of machine learning methods, e.g., Artificial Neural Network (ANN), in diabetes care, capable of learning physiological patterns based on a set of inputs and required outputs. Unexpected correlation • Influence of previous Blood Glucose Concentrations (BGC): • Final ANN performance: • 3.5% reduction in error, comparing to Linear Regression: 2. Objectives Rule based methods, such as the "Bolus Wizard", currently used to calculate the required insulin dose. These methods have proved to be inaccurate, since significant parameters are taken into consideration. Combining the two aspects of insulin delivery and glucose sensing encourages us to take advantage of these technologies and improve the clinical outcome of insulin delivery, operating in a semi-open mode, laying down the foundations for future closed loop systems – the "artificial pancreas“. In the scope of this project, we intend to investigate the practical use of ANNs, preferably for use in conjunction with the Solo™ MicroPump Insulin Delivery System. • Number of layers: • Most errors are for extreme values of the target: Validation Performance 3. Methods • Number of neurons in hidden layer – • simplest model that gives good performance: ANN Structure • ANN predicts output for unseen data based on learning patterns • Learning based on 3 examples sets: • 1. Training set – used for set weights between neurons • 2. Development set – used for choose the network architecture avoiding overfitting of the ANN to the training example • 3. Test set - used for evaluation of the ANN • ANN is composed of an input layer, one or more hidden layers • (a network with no hidden layers is called a perceptron) and an • output layer. • Each layer consists of one or more neurons and weights • connecting the layers 5. Discussion • Data collection - Simulated data has limited features and questionable fit to real • life data. Moreover, creating a benchmark is necessary for scientific progress. • Our ANN model is simple with reasonable error. Results show that complexity • does not improve performance. One main reason for this could be the • limitations of the synthetic data. • The learning algorithm updates the weights in order to minimize the error over the training set. In this way ANN • learns afunction connecting between input and output variables. • Data generated via AIDA software - simulating virtual diabetes patience. • Data sets based on one AIDA patient profile, as our objective is "tailored" insulin administration per patient. • Feature Analysis in a Perceptron: • ∆ Goal: input feature analysis and selection • ∆Weight analysis • ∆ Influence of previous BGCs 6. Future Work • Examining the effect of increasing the size of the training set. • Employ different combinations of the input parameters. • Adding a second ANN for predicting the corresponding blood-glucose • concentrations according to the first ANN output. • Introducing the ANN with real life data. • ANN Architecture • ∆ Number of neurons in each hidden layer • ∆ Number of layers • ∆ Transfer function