The paper describes the structure and the characteristics of an expert system that allows
the optimization of postprandial glycemia in type 1 diabetic patients. The expert system
is able to provide patients with the number of rapid insulin units that must be taken in
order to keep the blood glucose level close to the omeostatic condition in the hours
following a meal

Amato

Bergman

Campos-Delgado

Chee

DeFronzo

Galluzzo

Jang

Magni

Mamdani

Mathews

Mendel

Pankowska

Shapira

Sorensen

Sugeno

Leonardo Nobile

Bartolomeo Cosenza

Marco Amato

Valentina Guarnotta

Carla Giordano

Aldo Galluzzo

Mose Galluzzo

Crossref

Development of a fuzzy expert system for the control of glycemia in type 1 diabetic patients

The paper describes the structure and the characteristics of an expert system that allows
the optimization of postprandial glycemia in type 1 diabetic patients. The expert system
is able to provide patients with the number of rapid insulin units that must be taken in
order to keep the blood glucose level close to the omeostatic condition in the hours
following a meal

Nobile, L

COSENZA, Bartolomeo

AMATO, Marco Calogero

GUARNOTTA, Valentina

GIORDANO, Carla

GALLUZZO, Aldo

GALLUZZO, Mose'

Archivio della Ricerca - Università di Pisa

Development of a fuzzy expert system for the
control of glycemia in type 1 diabetic patients

Archivio istituzionale della ricerca - Università di Palermo

21st European Symposium on Computer Aided Process Engineering – ESCAPE 21E.N. Pistikopoulos, M.C. Georgiadis and A.C. Kokossis (Editors)© 2011 Elsevier B.V. All rights reserved.Development of a fuzzy expert system for the control of glycemia in type 1 diabetic patientsLeonardo Nobile a, Bartolomeo Cosenza a, Marco Amato b, Valentina Guarnotta b, Carla Giordano b, Aldo Galluzzo b, Mosè Galluzzo aaDipartimento di Ingegneria Industriale, Università degli Studi di Palermo, Viale delle  Scienze ed. 6, 90128, Palermo ItalybDipartimento di Oncologia Sperimentale ed Applicazioni Cliniche, Sezione di  Endocrinologia, Università degli Studi di Palermo, Piazza delle Cliniche 2, 90127 Palermo,ItalyAbstractThe paper describes the structure and the characteristics of an expert system that allows the optimization of postprandial glycemia in type 1 diabetic patients. The expert system is able to provide patients with the number of rapid insulin units that must be taken in order to keep the blood glucose level  close to the omeostatic condition in the hours following a meal.Keywords: fuzzy logic system, neuro fuzzy system, expert system, diabetes mellitus, glycemia control.1. IntroductionDiabetes mellitus is a disease characterized by high blood glucose level as a result of either the body not producing enough insulin, or the body cells not properly responding to the insulin that is produced (insulin-resistance). There are many types of diabetes, the most  common  of  which  are  type  1  diabetes  and  type  2  diabetes  (World  Health Organization, 2006). Type 1 diabetes results from the pancreatic beta-cells failure to produce insulin, and presently requires that patients inject themselves with insulin. Type 2 diabetes results from insulin resistance, a condition in which cells fail to properly use insulin,  combined with an absolute or relative insulin deficiency.  The expert  system presented in this paper is concerned with type 1 diabetes  only.  Patients with type 1 diabetes mellitus need external insulin, subcutaneously injected, for their survival.It is well known that patients with type 1 diabetes are exposed to frequent postprandial hyperglycemia or fasting and/or postprandial hypoglycemia. Two different approaches have been proposed to determine the correct dose of insulin to be  injected  by  diabetic  patients.  The  most  ambitious  approach  aims  to  provide  an automatic system that works on a continuous basis starting from the measurement of the blood glucose, calculating the correct dose of insulin that is then injected by a pump (Bergman et al., 1979; Sorensen; 1985; Magni et al.,2009; Chee and Fernando, 2007) The second approach instead intends to offer an off line system, called bolus calculator, able to calculate the correct dose of insulin to be assumed on the occasion of a meal. Systems based on the first approach present main problems related to the measurement devices and pumps, but also to the choice of a suitable control algorithm. Model based 2 L. Nobile et al.control  algorithms  are  the  most  promising  ones  but  the  development  of  reliable mathematical  models  of  the  involved  metabolic  processes  is  still  on.  The  second approach seems to be able to lead to a more rapid, reliable and cheaper system. The system here described follows this approach.Several  systems  have  been  already  proposed.  They  differ  for  the  variables  used  as inputs  and  outputs,  the  calculation  algorithm  and  the  validation  method.  Campos Delgado et al. (2006) proposed a system that works like a two loops off line control system. One control loop regulates the rapid insulin to be injected at meals time while the second loop works on a daily basis regulating the slow insulin. Their controller uses as input only the preprandial glycemia. The validation of the system has been carried out by simulation using a compartmental model. Mathews and Pelzer (2009) propose a bolus  calculator  that  takes  into  account  the  energy  of  carbohydrates,  the  metabolic efficiency and the dependence on the person of the energy that is transformed in blood glucose. Shapira et al. (2010) take into account as well the carbohydrate content of the meal  but  to  overcome  the  uncertainty  of  its  determination  they  propose  a  decision support tool that is based on the optimization of insulin doses for carbohydrate ranges. Pankowska and Blazik (2010) propose a bolus calculator that considers proteins and lipids  in  addition  to  carbohydrates  for  two  out  of  phase  insulin  injections  that  are justified by the different absorption time. The expert system developed by the authors, to be used by type 1 diabetic patients in the period between two medical visits, takes into account the content of the meal in terms of carbohydrates, lipids and proteins but also considers the patient insulin sensitivity. The system is conceived to provide patients with the number of rapid insulin units that may assure optimal glycemia values after a meal. To this end data concerning the meal, the physical and clinical personal conditions, including pre-prandial glycemia, and the last insulin prescription of the doctor, must be supplied to the system. This works like an off line mixed feedback-feedforward controller based on the measurement of blood glucose level (the output variable) and the measurement of the type and amount of the food taken on (main disturbances). The necessity of handling the patients meal food data, that are inherently fuzzy, and modeling a very complex biological system, for which reliable mathematical models are not yet available, suggested the use of fuzzy and neuro-fuzzy techniques for the implementation of the off line system.The development of the expert system was carried out on the basis of a set of clinical data specifically collected and consisted of three main phases: In the first phase a group of  type  1  diabetic  patients  were  involved,  for  a  period  of  time,  in  recording  data concerning their physical an clinical conditions, the type and amount of food taken on during their meals together with glycemia levels before and after them.The second phase consisted of the development of the hierarchical fuzzy system. The rules and the membership functions of some fuzzy subsystems were chosen on the base of the knowledge and experience of doctors. Experimental data, provided by patients, were  used  for  training  and  validating  the  neuro-fuzzy  subsystem.  The  third  phase consisted of testing the hierarchical fuzzy system with the experimental data.2. The expert system The expert system is constituted by three fuzzy systems and one adaptive neuro-fuzzy inference system (ANFIS) as shown in Fig.  1 and is  characterized by a hierarchical structure. Development of a fuzzy expert system for the control of glycemia in type 1 diabetic  patients3Fig.1. Block diagram of the Fuzzy System The inputs of the fuzzy system are the type and the amount of  food taken on during the meal  (converted  in  amounts  of  carbohydrates,  proteins  and  lipids),  the  preprandial glycemia,  the  number  of  rapid  insulin  units  recommended  by  the  therapy  and  the Visceral Adiposity Index (VAI), an index suggested by some of the authors (Amato et al., 2010) that estimates the patient insulin sensitivity. The VAI is gender specific, is calculated through the values of  triglycerides, high density lipoprotein (HDL)  and the patient  body  mass  index,  and  has  a  good  correlation  with  the  euglycemic hyperinsulinemic Clamp test (DeFronzo, 1979), considered as the Gold standard for the insulin sensitivity estimation.The first fuzzy system uses the Mamdani inference method (Mamdani, 1977) and is constituted by three inputs (Fig. 1) which are the amounts of carbohydrates, proteins and lipids assumed by the patient  during the meal.  Their values are evaluated  by a calculation module that uses the information about the type and amount of food  and the nutrition tables drawn by the Italian National Research Institute for Food and Nutrition (INRAN, 2010).  The output is  a  parameter  called “meal  effect  ” that  represents  the effect that the meal has on the blood glucose concentration. The second fuzzy system has two inputs and one output: the inputs are the “meal effect” (the output of the first fuzzy system) and the preprandial glycemia, a measurement that patients usually do before a meal, using a personal blood glucose meter.The  membership  functions  and  the  parameters  for  the  “meal  effect”  are  the  same provided  for  the  first  system,  while  there  are  five  membership  functions  for  the preprandial  glycemia.  The output represents the percentage change of the number of rapid insulin units prescribed by doctors in the traditional therapy that should be applied as consequence of the meal effect and the preprandial glycemia value. The third fuzzy system uses the Sugeno inference method (Sugeno and Kang, 1998). It has only one input and one output: the input is the VAI and the output is an additional percentage change of prescribed insulin units depending on the VAI. The sum of the two percentage changes calculated by the second and the third system, constitutes one of the two inputs of an ANFIS system (Jang, 1993). The second input is the number of the rapid insulin units prescribed by the doctors in the traditional therapy. The output is the number of units able to maintain the concentration of glucose in the blood close to the homeostatic condition. The ANFIS structure is trained making use of a  set  of  input-output  data:  the input  data  are  constituted of  the  percentage  changes calculated  by the previous fuzzy systems and the insulin units originally prescribed by doctors; the output, i.e. the target, is the number of optimal units suggested by expert 4 L. Nobile et al.doctors on the base of the knowledge of the clinical state (preprandial glycemia, VIA) and the meal characteristics. All the characteristics of the fuzzy systems (membership functions, input and output ranges, rules) were established on the basis of the experience and knowledge of several doctors. The proposed system has been tested on a set of 158 cases, 120 used for the training 38 for the validation. The graph in Fig. 2 a) shows the number  of  cases  with  a  given  error  (as  percentage  of  total  cases)  versus  the  error produced by the expert system (as percentage of prescribed insulin units). Notice that the percentage of cases with zero error is about 65.2 %.Fig. 2. a) Percentage of cases with a given error  vs. % error (percentage of  prescribed insulin units) produced by the expert system.  b)  Percentage of cases with a given error  vs. % error (percentage of  prescribed insulin units) when original prescribed insulin units are used.Fig. 2 b) shows the analogue graph if the original  doctor prescriptions are followed. Notice that the percentage of cases with zero error is much lower (about 33.5 %).3. ConclusionsThe results obtained with the proposed hierarchical fuzzy system are very encouraging. The expert system is in fact able to suggest the appropriate change of  insulin units to be injected after a meal. The reference used for the evaluation of the expert system is the prescription of expert  doctors  based on the same input data.  In  almost  all  cases  the system  is  able  to  suggest  the  same  therapy  change  indicated  by  the  doctors.  The performance  is  even  more  appreciable  if  the  comparison  is  made with  the  original doctor prescription.The performance could probably improve if the physical activity after the meal is also considered. Another improvement, in particular in the first subsystem, could come from the use of type-2 fuzzy sets, that have a higher performance in dealing with uncertain data (Mendel 2001, Galluzzo and Cosenza, 2010), as the meal data. The  system  could  be  tuned  also  for  a  specific  patient  through  an  off  line  further adaptation of the ANFIS module.ReferencesAmato, M., Giordano, C., Galia, M., Criscimanna, A., Vitabile, S., Midiri, M. and Galluzzo, A., 2010, Visceral Adiposity Index (VAI): a Reliable Indicator of Visceral Fat Function Associated with Cardiometabolic Risk, Diabetes Care, 33(4):920-2.Development of a fuzzy expert system for the control of glycemia in type 1 diabetic  patients5Bergman, R. N., Ider, Y. Z., Bowden C. R. and Cobelli C., 1979, Quantitative estimation of insulin sensitivity, American Journal of Physiology, 236, 6, 667-677.Campos-Delgado, D. U., Hernández-Ordoñez, M., Femat, R., and Gordillo-Moscoso, A., 2006, Fuzzy-Based Controller for Glucose Regulation in Type-1 Diabetic Patients by Subcutaneous Route. IEEE Transactions on biomedical engineering, 53 (11), 2201-2210.Chee, F. and Fernando, T., 2007, Closed-Loop Control of Blood Glucose,  Lecture Notes in Control and Information Sciences, Springer-Verlag, Berlin.DeFronzo, R. A., Tobin, J. D. and Andres, R., 1979, Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am. J. Physiol. Gastrointest. Liver Physiol., 237, G214-G223.Galluzzo, M., Cosenza, B., 2010, Adaptive Type-2 Fuzzy Logic Control of a Bioreactor, Chemical Engineering Science, 65, 4208–4221.INRAN, 2010, http://www.inran.it).Jang, J. S. R., 1993, ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on Systems, Man, and Cybernetics, 23, 3, 665-685.Magni, L., Raimondo, D.M., Dalla Man, C., De Nicolao, G., Kovatchev, B. and Cobelli C., 2009, Model predicative Control of glucose concentration in type I diabetic patients: An in silico trial, Biomedical Signal Processing and Control, 4, 338-346.Mamdani, E. H., 1977, Applications of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers, 26, 12, 1182-1191.Mathews, E. H. and Pelzer, R., 2009, A New Model to Estimate Bolus Insulin Need”. Diabetes Technology & Therapeutics, 11(12), 813-817.Mendel, J. M., 2001, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Upper Saddle River, NJ: Prentice-Hall.Pankowska, E. and Blazik, M., 2010, Bolus calculator with nutrition database software, a new concept of prandial insulin programming for pump user, Journal of Diabetes Science and Technology, 4( 3), 571-576.Shapira, G., Yodfat, O., HaCohen, A., Feigin, P. and Rubin R., 2010, Bolus Guide: A novel Insulin Bolus Dosing Decision Support Tool Based on Selection of Carbohydrate Ranges, Journal of Diabetes Science and Technology, 4 (4), 893-902.Sorensen, J. T., 1985, A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes, Ph.D thesis, Dept. Chem. Eng., Massachusetts Institute of Technology, Cambridge.Sugeno, M. and Kang, G. T, 1998, Structure identification of Fuzzy Model, Fuzzy sets and systems, 28,1, 15-33. World Health Organization, 2006. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Report of a WHO/IDF Consultation. Geneva, World Health Organization.

Development of a fuzzy expert system for the
control of glycemia in type 1 diabetic patients

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Development of a fuzzy expert system for the control of glycemia in type 1 diabetic patients

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