A neural network approach for insulin regime and dose adjustment in type 1 diabetes.
BACKGROUND
A decision support system based on a neural network approach is proposed to advise on insulin regime and dose adjustment for type 1 diabetes patients.


METHODS
The system consists of two feed-forward neural networks, trained with the back-propagation algorithm with momentum and adaptive learning rate. The input to the system consists of patient's glucose levels, insulin intake, and observed hypoglycemia symptoms during a short time period. The output of the first neural network provides the insulin regime, which is applied as input to the second neural network to estimate the appropriate insulin doses for a short time period.


RESULTS
The system's ability in order to recommend on insulin regime is excellent, while its performance in adjusting the insulin dosages for a specific patient is highly dependent on the data set used during the training procedure.


CONCLUSIONS
Despite the limitations of computer-based approaches, this study shows that artificial neural networks can assist diabetes patients in insulin adjustment.
Mougiakakou
S G
SG
Department of Electrical and Computer Engineering, National Technical University of Athens, Greece.
Nikita
K S
KS
D001786:Blood Glucose / Q000032:analysis / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring; D019992:Databases as Topic; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy*; D004334:Drug Administration Schedule; D004435:Eating; D006801:Humans; D007003:Hypoglycemia / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage* / Q000627:therapeutic use; D009415:Nerve Net
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin