Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2017 / Article
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Artificial Intelligence in Medical Applications

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Research Article | Open Access

Volume 2017 |Article ID 4307508 | https://doi.org/10.1155/2017/4307508

Rung-Ching Chen, Hui Qin Jiang, Chung-Yi Huang, Cho-Tsan Bau, "Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis", Journal of Healthcare Engineering, vol. 2017, Article ID 4307508, 14 pages, 2017. https://doi.org/10.1155/2017/4307508

Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis

Academic Editor: Weide Chang
Received02 Jul 2017
Accepted10 Sep 2017
Published26 Oct 2017

Abstract

Introduction. Although a number of researchers have considered the positive potential of Clinical Decision Support System (CDSS), they did not consider that patients’ attitude which leads to active treatment strategies or HbA1c targets. Materials and Methods. We adopted the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) published to propose an HbA1c target and antidiabetic medication recommendation system for patients. Based on the antidiabetic medication profiles, which were presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE), we use TOPSIS to calculate the ranking of antidiabetic medications. Results. The endocrinologist set up ten virtual patients’ medical data to evaluate a decision support system. The system indicates that the CDSS performs well and is useful to 87%, and the recommendation system is suitable for outpatients. The evaluation results of the antidiabetic medications show that the system has 85% satisfaction degree which can assist clinicians to manage T2DM while selecting antidiabetic medications. Conclusions. In addition to aiding doctors’ clinical diagnosis, the system not only can serve as a guide for specialty physicians but also can help nonspecialty doctors and young doctors with their drug prescriptions.

1. Introduction

The Institute of Medicine [1] defines patient-centered care strategy as “providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions.” Clinical practitioners need to select different drugs to meet the needs of patients. However, patients with type 2 diabetes mellitus exhibit tremendous differences in phenotypes resulting in significant heterogeneity in clinical results. Consequently, clinical practitioners cannot be certain whether a prescription for a particular patient is the best.

Clinical decision support system (CDSS) may help clinicians, patients, and others to suggest patient-appropriate evidence-based treatment options. Ontologies are essential tools for the organization and representation of knowledge [27]. Ontologies contain the collection of patients, symptoms, diseases, diagnoses, treatments, and drug information, thereby creating a healing strategy according to patient’s requirements to reconfigure a clinical decision support system [8]. Some of the studies suggested using ontologies to build clinical guidelines and care plans [5, 912].

In most of the knowledge ontologies, there is a design by the experience of domain experts. For example, Bau et al. [2] used domain ontology and rule reasoning to construct a CDSS for diabetic patients undergoing surgery. They have three main classes in this ontology: disease, management, and patient. The disease class consists of diabetes and comorbidity information. The management class consists of anesthesia, capillary glucose tests, control of DM, medication, no medication, and water restriction information. The patient class consists of the patient clinical profile. The system constructs a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. However, the ontology knowledge is built on the experience of clinical practitioners, so it is hard to update these ontologies knowledge when there is a new clinical guideline.

Sherimon and Krishnan [11] had proposed an OntoDiabetic system which an ontology-based clinical decision support system for risk analysis and prediction of diabetes mellitus. The system consists of two main ontologies: the diabetic patient clinical analysis ontology and the semantic profile. The diabetic patient clinical analysis ontology and reasoning rules encapsulate the NICE (National Institute for Health and Care Excellence) guidelines. The OntoDiabetic system calculates the score and predicts the risk of diabetic patients due to smoking, alcohol, physical activity, and sexual and cardiovascular diseases that mainly affect diabetes. Lots of effort was made on OntoDiabetic. What seems to be lacking, however, is that the system cannot provide antidiabetic medications suggestion.

Because there are many types of antidiabetic medications, they need to obtain permission from the government. Although the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) statements [13, 14] provide 12 types of antidiabetic medications, not every drug can be used. Table 1 shows eight antidiabetic medications which are commonly used in Japan, Korea, Canada, Italy, and Taiwan from 1998 to 2013 [1521].


Authors(Publication year) titleSource period (year)CountryAntidiabetic medications

Chang et al. [15](2012)
National trends in antidiabetic treatment in Taiwan, 2000–2009
2000–2009TaiwanBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, insulin
Abdelmoneim et al. [20](2013)
Use patterns of antidiabetic regimens by patients with type 2 diabetes
1998–2010CanadaBiguanides, SU, Meglitinides, TZDs, α-glucosidase, insulin
Kohro et al. [17](2013)
Trends in antidiabetic prescription patterns in Japan from 2005 to 2011—impact of the introduction of dipeptidyl peptidase-4 inhibitors
2005–2011JapanBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, GLP-1, insulin
Hsu et al. [21](2015)
Utilization of oral antidiabetic medications in Taiwan following strategies to promote access to medicines for chronic diseases in community pharmacies
2001–2010TaiwanBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, GLP-1
Rafaniello et al. [18](2015)
Trends in the prescription of antidiabetic medications from 2009 to 2012 in a general practice of Southern Italy: a population-based study
2009–2012ItalyBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, GLP-1, insulin
Ko et al. [19](2016)
Trends of antidiabetic drug use in adult Type 2 diabetes in Korea in 2002–2013: nationwide population-based cohort study
2002–2013KoreaBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, insulin
Ou et al. [16](2016)
Recent trends in the use of antidiabetic medications from 2008 to 2013: a nationwide population-based study from Taiwan
2008–2013TaiwanBiguanides, SU, Meglitinides, TZDs, α-glucosidase, DPP-4, GLP-1, insulin

SU: sulfonylureas; TZDs: thiazolidinediones; DPP-4: dipeptidyl peptidase-4; SGLT2: sodium-glucose cotransporter 2; GLP-1: glucagon-like peptide 1.

Although a number of researchers have considered the positive potential of CDSS, they did not consider patients’ characteristics. For example, at the “patients’ attitude” factor, if the patient has highly motivated or excellent self-care capacities, it can use active treatment strategies or HbA1c targets. To solve this problem, we proposed a solution in our previous research which adopted the ADA and the EASD standards who published an updated position statement on the management of hyperglycemia in type 2 diabetes to build HbA1c target inference module as well as drug knowledge ontology [22]. The system combines fuzzy logic and ontology reasoning to propose an antidiabetic medication recommendation system for patients with T2DM.

In this paper, we will further consider the safety and positivity of HbA1c target, and the priority of antidiabetic medication. We use the antidiabetic medication profiles, which are presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) in 2016 [23]. Based on the antidiabetic medication profiles, we used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to calculate the relative closeness to the ideal solution and thus determined the ranking of antidiabetic medications.

2. Materials and Methods

The recommendation system consists of four modules: the patient consultation management, the patient perfect HbA1c target inference, the drug knowledge ontology and reasoning, and antidiabetic medication ranking modules. The framework of the recommendation system is shown in Figure 1.

The first step, the “patient consultation management module,” provides a user interface to the clinical doctor. So, the clinical doctor can input patient’s data in the user interface. Those patient’s data will be provided to the other two modules. The second step, the “patient ideal HbA1c target inference module,” will use fuzzy technology to infer the patient’s individualization HbA1c target. The third step, the “drug knowledge ontology and reasoning module,” will recommend antidiabetic medications for the patient. The fourth step, the “antidiabetic medications ranking module,” will use TOPSIS technology to calculate the relative closeness to the ideal solution and thus determine the ranking of antidiabetic medications. In this system, the experts of diabetes decided the fuzzy rules and ontology reasoning rules.

2.1. Patient Consultation Management Module

The patient consultation management module requires the patients’ data which is also necessary for the other modules. The modulation of the intensiveness of glucose lowering therapy in T2DM is according to the ADA and the EASD position statement [14]. The sufficient communication between the clinical doctor and the patient is also necessary to evaluate seven factors. They are (1) the risks associated with hypoglycemia and other drug adverse effects, (2) disease duration, (3) life expectancy, (4) important comorbidities, (5) established vascular complications, (6) patient attitude and expected treatment efforts, and (7) resources and support system. Each of the seven factors has five levels measured by integers 0 to 4. The clinical doctor also needs to record adverse drug reactions (ADRs) and individual history of diseases.

2.2. Patient Ideal HbA1c Target Inference Module

The main functional modules include fuzzifier, fuzzy rules, fuzzy inference, and defuzzier. There are seven inputs, namely, , for fuzzy logic and the input factors are divided into five levels, ranging from integers 0 to 4. The output value z is the ideal patient HbA1c target level which considers individual differences. The American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) suggested HbA1c below 6.5% [24], but patient-centered care is needed to consider the patient’s characteristics to set the patient’s HbA1c target. So, the output z represents the ideal HbA1c target, which varies between 6.5% and 9.0%.

The definition of membership functions is according to the ADA and EASD position statement [14], for example, the “Risks potentially associated with hypoglycemia and other drug adverse effects” can have two levels: “Low” or “High.” So, x1 has two membership functions: Low(x1) and High(x1). The names of the membership functions as well as input and output variables are shown in Table 2. To get the acceptable results, the endocrinologist who works in Taichung Hospital, Ministry of Health and Welfare, has established virtual patients’ data and use the FuzzyLite [25] to trial and adjust the parameters of membership functions. Through the sufficient experience of the clinician, the system has better results.


VariableNameFunction 1Function 2Function 3

x1Risks potentially associated with hypoglycemia and other drug adverse effectsLowHigh
x2Disease durationNewly DiagnosedLong Standing
x3Life expectancyLongShort
x4Important comorbiditiesAbsentFewOrMildSevere
x5Established vascular complicationsAbsentFewOrMildSevere
x6Patient attitude and expected treatment effortsHighly MotivatedLess Motivated
x7Resources and support systemReadily AvailableLimited
zHbA1cMore StringentMild StringentLess Stringent

Figure 2 shows the corresponding membership function for x1 factor, and the membership function for Low(x1) and High(x1) is a trapezoid. Because the x2, x3, x6, and x7 also can be divided into two functions, their membership functions are the same as that for x1. Figure 3 shows the corresponding membership function for x4 factor, and the membership function for Absent(x4) and Severe(x4) is trapezoidal while FewOrMild(x4) is triangular. Because the x5 also can be divided into three functions, x5 membership functions are the same as x4. The domain knowledge of the defining membership functions is derived from doctors’ reports. Figure 4 shows the corresponding membership function for z and the membership functions for MoreStringent(z) and LessStringent(z) are trapezoidal while that for MildStringent(z) is triangular.

The second step is to apply inputs to the fuzzy rules. The fuzzy inference will then stipulate what action will be taken for each combination of sets of memberships. To evaluate the effectiveness of the system, we developed two kinds of fuzzy rule methods. The primary consideration for the first method is relative safety of treatment so we label it “fuzzy safety rules.” The second method is to consider the performance of more positive treatment; we mark it “fuzzy positivity rules.”

2.2.1. Method 1: Fuzzy Safety Rules

The number of fuzzy rules depends on several input factors. For example, if the clinical doctor inputs ×1, ×2, and ×4 values, the fuzzy rules will consist of 12 individual rules. Because ×1 has two membership functions (low and high), ×2 has two membership functions (Newly Diagnosed and Long Standing), and ×4 has three membership functions (Absent, Few or Mild, and Severe). Based on individual experts’ experience and intuition, the fuzzy rules table is shown in Table 3. Rule 1 indicates that if x1 is low and x2 is newly diagnosed, and x4 is absent, then z is more stringent. Rule 2 states that if x1 is low and x2 is newly diagnosed, and x4 is few or mild, then z is mild stringent. Otherwise, the output z is less stringent in rules 3–12 because x1 is high, or x2 is long standing, or x4 is severe.


Rulex1x2x4z

1LowNewly DiagnosedAbsentMore Stringent
2LowNewly DiagnosedFewOrMildMild Stringent
3LowNewly DiagnosedSevereLess Stringent
4LowLong StandingAbsentLess Stringent
5LowLong StandingFewOrMildLess Stringent
6LowLong StandingSevereLess Stringent
7HighNewly DiagnosedAbsentLess Stringent
8HighNewly DiagnosedFewOrMildLess Stringent
9HighNewly DiagnosedSevereLess Stringent
10HighLong StandingAbsentLess Stringent
11HighLong StandingFewOrMildLess Stringent
12HighLong StandingSevereLess Stringent

2.2.2. Method 2: Fuzzy Positivity Fuzzy Rules

Table 4 shows the fuzzy positivity rules. Rules 1–5 indicate that if one of the input variables is High/Long-Standing/Short/Severe/Severe, then HbA1c is less stringent. Rules 6–9 indicate that if one of the input variables is Newly-Diagnosed/Long/Highly-Motivated/Readily-Available, then HbA1c is more stringent. Rule 10 indicates that if the “Risks-Of-Hypoglycemia-or-Drug-Effects” are low and “Important-Comorbidities” and “Established-Vascular-Complications” are absent, then HbA1c is More-Stringent. Rules 11 and 12 show that if “Important-Comorbidities” or “Established-Vascular-Complications” are Few-or-Mild, then HbA1c is Mild-Stringent. Rule 13 states that if both of “Important-Comorbidities” and “Established-Vascular-Complications” are “Few-or-Mild,” then HbA1c is Less-Stringent.


RuleFunction

1If (Risks-Of-Hypoglycemia-or-Drug-Effects is High) then (HbA1c is Less-Stringent)
2If (Disease-Duration is Long-Standing) then (HbA1c is Less-Stringent)
3If (Life-Expectancy is Short) then (HbA1c is Less-Stringent)
4If (Important-Comorbidities is Severe) then (HbA1c is Less-Stringent)
5If (Established-Vascular-Complications is Severe) then (HbA1c is Less-Stringent)
6If (Disease-Duration is Newly-Diagnosed) then (HbA1c is More-Stringent)
7If (Life-Expectancy is Long) then (HbA1c is More-Stringent)
8If (Patient-Attitude is Highly-Motivated) then (HbA1c is More-Stringent)
9If (Resources-and-Support-System is Readily-Available) then (HbA1c is More-Stringent)
10If (Risks-Of-Hypoglycemia-or-Drug-Effects is Low) and (Important-Comorbidities is Absent) and (Established-Vascular-Complications is Absent) then (HbA1c is More-Stringent)
11If (Important-Comorbidities is Few-or-Mild) then (HbA1c is Mild-Stringent)
12If (Established-Vascular-Complications is Few-or-Mild) then (HbA1c is Mild-Stringent)
13If (Important-Comorbidities is Few-or-Mild and (Established-Vascular-Complications is Few-or-Mild) then (HbA1c is Less-Stringent)

Finally, for both fuzzy safety rules and fuzzy positivity rules, the system uses the mean of maximum (MOM) to perform defuzzification.

2.3. Drug Knowledge Ontology and Reasoning Module

Protégé and WebProtégé are free software programs for building ontology knowledge solutions [6, 26]. Further, “Jena” is the Java rule-based inference engine developed by Apache Software Foundation [27]. We use WebProtégé to build drug knowledge, and the web-based interface is an easy interface with a diabetes diplomate. When the ontology build is complete, we use Jena to evaluate the antidiabetic medications reasoning module. The details are as follows.

2.3.1. Drug Knowledge Ontology

According to an update of the position statement published by the ADA and the EASD [13, 14], we created a glucose-lowering agents ontology. Table 5 shows the classes and the descriptions of their concepts in the domain knowledge. Classes can contain individual objects called “instances.” Table 6 presents the defined properties in the ontology. Object properties represent relationships between two instances and each property has a domain and range. After classes and object properties are created, we build glucose-lowering agent instances based on the ADA and the EASD’s position statement on the management of hyperglycemia in type 2 diabetes. Figure 5 shows “Biguanides” instances of the “Glucose-Lowering_Agents” class and Figure 6 shows an example of patient_1’s instance.


ClassDescription

Glucose-Lowering_AgentsConcepts are glucose-lowering drugs. Ontology content is based on the ADA/EASD’s position statement on management of hyperglycemia in type 2 diabetes to be established
Glucose-Lowering_AdvantagesConcepts about glucose-lowering advantages
Glucose-Lowering_Cellular_mechanismsConcepts about glucose-lowering cellular mechanisms
Glucose-Lowering_CompoundsConcepts about glucose-lowering compounds
Glucose-Lowering_CostConcepts about glucose-lowering cost
Glucose-Lowering_DisadvantagesConcepts about glucose-lowering disadvantages
Glucose-Lowering_Primary_physiological_actionsConcepts about glucose-lowering primary physiological actions
PatientsConcepts about patient’s profile, the properties include patient’s adverse drug reactions (ADRs) and history of diseases


Property nameProperty typeDomainRange

has_AdvantagesObjectGlucose-Lowering_AgentsGlucose-Lowering_Advantages
has_Cellular_mechanismsObjectGlucose-Lowering_AgentsGlucose-Lowering_Cellular mechanisms
has_CompoundsObjectGlucose-Lowering_AgentsGlucose-Lowering_Compounds
has_CostObjectGlucose-Lowering_AgentsGlucose-Lowering_Cost
has_DisadvantagesObjectGlucose-Lowering_AgentsGlucose-Lowering_Disadvantages
has_Primary_physiological_actionsObjectGlucose-Lowering_AgentsGlucose-Lowering_Primary physiological_actions
has_History_of_DiseasesObjectPatientsGlucose-Lowering_Disadvantages
has_Adverse
Drug_Reactions
ObjectPatientsGlucose-Lowering_Agents
Not_recommendedObjectPatientsGlucose-Lowering_Agents
ID_NoDataPatientsxsd: string

2.3.2. Antidiabetic Medications Reasoning Module

Jena is a free and open source Java framework for building semantic web and inference applications [27]. The Jena inference engines support the use of Jena rules to infer from instance data and class descriptions.

Jena is a rule inference engine running on the Java platform. This study developed Jena rules for reasoning which Glucose-Lowering_Agents are not suitable for patients. Table 7 shows the rules described as follows: Rule 1:If patients have a history of disease which is related to the disadvantages of Glucose-Lowering_Agents, the Glucose-Lowering_Agents are not recommended.Rule 2:If patients have adverse drug reactions (ADRs), the ADRs are not recommended.

When the system removes some antidiabetic medications, the system can determine other antidiabetic medications. For example, patient_1 has a history of “increasing_LDL-C” and “Edema.” TZDs has both disadvantages, “increasing_LDL-C” and “Edema,” one of SGLT2’s disadvantages is “increasing_LDL-C.” By Rule 1, TZDs and SGLT2 will not be recommended to patient_1. patient_1 also has ADRs with GLP-1. Thus, by Rule 2, GLP-1 will not be recommended to patient_1. This system provides seven common antidiabetic medications in Taiwan which include Biguanides (Metformin), Sulfonylureas (SU), TZDs, DPP-4, SGLT2, GLP-1, and Insulin. When TZDs, SGLT2, and GLP-1 are not recommended to patient_1, Biguanides (Metformin), DPP-4, Sulfonylureas (SU), and Insulin are recommended.

2.4. Define Risk of Antidiabetic Medications

The American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE) published an algorithm for determining glycemic control in 2009 [28]. The comprehensive diabetes management algorithm was updated in 2013, 2015, and 2016 [23, 24, 2931]. One significant contribution was the presentation of antidiabetic medication profiles. It shows each drug’s properties considered for patients.

According to the antidiabetic medication profiles, we convert seven traditional antidiabetic drugs to present the antidiabetic medication risk values. Table 8 shows the risks of antidiabetic medications. The risk value of “Few adverse events of possible benefits” is 1. The risk value of “Neutral” is 3 and “Use with caution” is 5. Finally, the “Likelihood of adverse effects” is defined as a risk value of 7. Of note, we added “cost” property values according to the position statement of the ADA and the EASD [14]. If cost is “Low,” the property value is 1. If cost is “High,” the property value is 3. In this case, there are some antidiabetic medications like “Meglitinides,” “α-glucosidase inhibitors” whose cost is “Moderate” so the property value is 2. However, these antidiabetic medications are not popular in Taiwan, so they do show in Table 8.


PropertiesAntidiabetic medications
METGLP-1SGLT2DPP-4TZDSUInsulin

Hypo3333377
Weight1113577
Renal/GU7753377
GI Sx5533333
CHF3333533
CVD1333353
Bone3333533
Cost1333113

MET: metformin (Biguanides); SU: sulfonylureas; Hypo: hypoglycemia; GU: genitourinary; GI Sx: glycemic index symptom; CHF: congestive heart failure; CVD: cardiovascular diseases.
2.5. TOPSIS Multicriteria Decision Analysis

When the risk of antidiabetic medications is known, we can use it to calculate the antidiabetic medication recommended priority. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) implements a multicriteria decision which was developed by Hwang and Yoon in 1981 [4, 3234]. TOPSIS was employed to decide antidiabetic medications ranking.

In previous calculations, the system recommended MET (Biguanides), DPP-4, SU (Sulfonylureas), and Insulin for patient_1. Table 9 shows the risk of antidiabetic medications and cost for patient_1. We will use the risk data of Table 9 as an example to explain the TOPSIS method.


Antidiabetic medicationsProperties
HypoWeightRenal/GUGI SxCHFCVDBoneCost

MET31753131
DPP-433333333
SU77733531
Insulin77733333

The TOPSIS process of patient_1 was carried out as follows.

Step 1: Create the Decision Matrix. Create an evaluation matrix consisting of alternatives and criteria with the intersection of each alternative and criteria are given as . For example, the decision matrix of Table 9 is

Step 2: Construct Normalized Decision Matrix. The matrix is then normalized to form the matrix: where .

Step 3: Determine the Weight. Determine the weight with the antidiabetic medication’s risk properties and cost. The initial weight ; is weight of the Hypo property; is weight of Weight property; is weight of Renal/GU property; is weight of GI Sx property; is weight of CHF property; is weight of CVD property; is weight of Bone property; and is weight of Cost property.
In this case, patient_1 has a history of “Edema” and “increasing LDL-C.” Because “Edema” is relative to CHF, the is set to 2, and “increasing LDL-C” is relative to CVD, the set to 2. The weight of patient_1 is shown as follows:

Step 4: Construct the Weighted Normalized Decision Matrix. Calculate the weighted normalized decision matrix : where

Step 5: Determine the Ideal and Negative Ideal Solutions. Determine the ideal solution and the negative ideal solution : where . where

Step 6: Calculate the Separation Measures for Each Alternative. Calculate the distance between the target alternative and ideal alternative and the negative ideal alternative: From the above formula, the system can find the values of and as follows:

Step 7: Calculate the Relative Closeness to the Ideal Solution . From the above formula, the system will select the options with closest to 1. The results show that the ideal solution of MET is 0.775, the ideal solution of DPP-4 is 0.540, the ideal solution of SU is 0.270, and the ideal solution of the Insulin is 0.384. Because , the recommendation priority of antidiabetic medications is MET > DPP-4 > Insulin > SU.

3. Results

According to the ADA and the EASD’s statement, one of the major changes in treatment options is a new antidiabetic medication “SGLT2” which is added. So, we add “SGLT2” to this experiment. However, “Meglitinides” and “α-Glucosidase” have “Frequent dosing schedule” disadvantage, so we exclude those two antidiabetic medications. Finally, this system provides seven common antidiabetic medication choices which include “Biguanides,” “SU,” “TZDs,” “DPP-4,” “SGLT2,” “GLP-1,” and “Insulin.”

Taichung Hospital is an accredited area hospital in central Taiwan. At the beginning of the system development, the endocrinologist who works in Taichung Hospital set up ten virtual patients’ medical data to trial and adjusted clinical decision support system (CDSS). We used Mamdani-type fuzzy inference and mean of maximum (MOM) to perform defuzzification. Table 10 shows the virtual patient’s medical data. In Table 10, the , “History of Diseases” and “ADRs” are input variables for diabetes diplomat. The z is the fuzzy inference output of the HbA1c target which, respectively, are fuzzy safety rules (Method 1) and fuzzy positivity rules (Method 2). The “Recommend antidiabetic medications” shows the recommended medications for patients and the ranking value.


IDAgeSexx1, x2, x3, x4, x5, x6, x7has_History of Diseaseshas_ADRsz (method 1)z (method 2)Recommended antidiabetic medications

173Female3, 2, 3, NaN, NaN, NaN, NaNincreasing_LDL-C, EdemaGLP-18.68.9(1) Biguanides (0.775)
(2) DPP-4 (0.540)
(3) Insulins (0.384)
(4) Sulfonylureas (0.270)
275Female3, 2, 4, NaN, NaN, NaN, NaNHeart_failure, increasing_LDL-CNaN8.69.0(1) Biguanides (0.788)
(2) DPP-4 (0.549)
(3) GLP-1 (0.536)
(4) Insulins (0.376)
(5) Sulfonylureas (0.248)
364Female2, 1, 2, NaN, NaN, NaN, NaNBone_fractures, increasing_LDL-CNaN6.96.6(1) Biguanides (0.788)
(2) DPP-4 (0.549)
(3) GLP-1 (0.536)
(4) Insulins (0.376)
(5) Sulfonylureas (0.248)
476Female4, 3, 3, 2, 1, NaN, NaNincreasing_LDL-C, Contraindications_CKDDPP-48.87.8(1) GLP-1 (0.631)
(2) Insulins (0.445)
(3) Sulfonylureas (0.369)
561Female4, 3, 2, 3, 2, NaN, NaNHeart_failure, increasing_LDL-C, Contraindications_CKD, Weight_gainNaN8.67.8(1) GLP-1 (0.534)
(2) DPP-4 (0.466)
664Female2, 1, 1, NaN, NaN, 2, NaNNaNNaN6.96.6(1) Biguanides (0.731)
(2) SGLT2 (0.648)
(3) DPP-4 (0.619)
(4) GLP-1 (0.586)
(5) TZDs (0.549)
(6) Sulfonylureas (0.377)
(7) Insulins (0.365)
762Male2, 2, 3, NaN, NaN, 3, 1Gastrointestinal_side_effects_abdominal_cramping, increasing_LDL-CNaN8.66.6(1) DPP-4 (0.703)
(2) GLP-1 (0.543)
(3) Insulins (0.481)
(4) Sulfonylureas (0.417)
881Female4, 3, 4, 4, 4, 4, 2MI, increasing_LDL-C, Contraindications_CKDDPP-48.69.0(1) GLP-1 (0.631)
(2) Insulins (0.445)
(3) Sulfonylureas (0.369)
948Female1, 1, 2, 3, NaN, NaN, 1Patient_reluctance_about_injection, increasing_LDL-CNaN7.96.6(1) Biguanides (0.796)
(2) GLP-1 (0.560)
(3) DPP-4 (0.558)
(4) Sulfonylureas (0.248)
1056MaleNaN, 2, 2, 2, 1, 1, NaNWeight_gain, increasing_LDL-C, Gastrointestinal_side_effects_nauseaTZDs7.97.8(1) Biguanides (0.687)
(2) DPP-4 (0.313)

An attending physician, an endocrinologist, and a resident physician evaluated the decision support system for diabetes. They were direct using the system and evaluation it by their clinical experience. They evaluated the system using a 12-question, 5-point survey, regarding satisfaction degree, perceived usefulness, and behavioral intentions (see Table 11). All the scores are expressed as percentage. The evaluation results are shown in Table 12. The clinical decision support system (CDSS) perceived 73% satisfactions. The results of antidiabetic medication recommendation indicate that the system has 70% satisfaction and 71% has intentions to use it.


QuestionScoring
12345

What do you think about “Patient ideal HbA1c target inference”?
Are you satisfied with its accuracy?
Is the “Method 1: Safety fuzzy rules” accurate?
Are you satisfied with the results of the “Method 1: Safety fuzzy rules”?
Is the “Method 2: Positivity fuzzy rules” accurate?
Are you satisfied with the results of the “Method 2: Positivity fuzzy rules”?
What do you think about “Antidiabetic medications reasoning and ranking”?
Are you satisfied with its accuracy?
Is the “Antidiabetic medications reasoning and ranking” accurate?
Are you satisfied with the results of the “Antidiabetic medications reasoning and ranking”?
Do you think the system can provide some benefits for you?
Using the system improves my performance in my job.
Using the system enhances my effectiveness in my job.
I find the system to be useful in my job.
If this system used in conjunction with the actual work, would you continue to use this system at work?
I enjoy using this system at work.
I will frequently use this system in the future.
I will strongly recommend to others to use this system.

Title: ○Endocrinologists ○Attending physicians ○Resident physicians
Gender: ○Male ○Female
E-mail:

ScoresParticipants
Endocrinologist
Attending physician
Resident physician
Endocrinologist
Attending physician

HbA1c target inference (Safety fuzzy rules: Method 1)” Satisfaction degree (%)67%80%
HbA1c target inference (Positivity fuzzy rules: Method 2)” Satisfaction degree (%)67%60%
“Antidiabetic medications reasoning and ranking” Satisfaction degree (%)70%85%
Perceived usefulness (%)73%87%
Intentions to use (%)71%77%

According to the feedback of resident physicians, because the inpatients may have too many complications, so the resident physicians mostly use insulin to control HbA1c. Therefore, the evaluation results of the resident physician will be relatively weak; this is because the recommendation system is only suitable for outpatients. So, if we exclude the results of the assessment of the resident physicians, the evaluation results of the system will be better. In this situation, the participating clinicians have 87% acceptance, and the likelihood of using the system at work and recommending it to others is 77%.

The fuzzy safety rules (Method 1) has 80% accuracy and satisfaction, but the fuzzy positivity rules (Method 2) is only 60%. So, the fuzzy safety rules (Method 1) is better than the fuzzy positivity rules (Method 2) for the patient ideal HbA1c target inference. The evaluation result of “Antidiabetic medications reasoning and ranking” shows that the system has 85% satisfaction which can assist clinicians to the management of T2DM while selecting antidiabetic medications.

The user interface for the antidiabetic medication recommendation system is shown in Figure 7. The website of the system is http://120.109.46.42/T2DM/. Doctors may refer to the system to make prescriptions. Of course, the system, which is likely to make misleading or inappropriate suggestions, cannot replace a doctor’s clinical experience and professional judgment. The doctor thus makes the final decision.

4. Discussion

The number of patients with diabetes worldwide is significant and continually increasing. Diabetes imposes psychological, physical, and financial hardship on patients. Diabetes therapy, no doubt, is a complicated task. As regards the prescription strategy of clinical doctors, it is necessary that they consider many factors. However, the following two reasons will affect the doctor’s decision to use the system: (1)If a doctor uses the new and expensive drugs as a treatment prescription, he will worry that the health insurance will not pay medical expenses. Then, he will only use the generic antidiabetic drugs.(2)The system only provides a single drug treatment prescription, for long-term diabetes patients may need a multidrug treatment prescription to reduce HbA1c effectively.

Even so, CDSS is used to assist humans in making decisions rather than replacing human decisions. The system shows the following clinical values: (a)Define appropriate therapeutic goals implementing patient-centered medical care and prescriptions:The patient-centered management strategy, by contrast, holds that not all patients can benefit from active glucose management. It stresses individualized therapeutic goals. However, diabetes, multiple complications, and the complexity inherent in antidiabetic medication use often make it difficult for doctors, especially young doctors, to select the best therapeutic strategy. Despite their awareness of the concept of “patient-centered management strategy,” it has shown the difficulty in practice. Given this, we systematized the constructs to help doctors develop their therapeutic goals and selection of prescriptions to meet the patient’s needs. In addition to encouraging patients to follow doctors’ instructions, this method can also reduce the risks resulting from medical treatment. Therapeutic goals may thus be achieved.(b)Doctors can save time and make the best use of medical resources:The increasing number of patients with type 2 diabetes has been exhausting medical resources. This system can enable doctors to spend less time on medical diagnosis and adjustment of patients’ prescriptions. This will reduce the impact on health care resources.(c)Doctors can employ the system with ease, and their clinical inertia can be reduced:This system is manipulation-friendly. By inputting a few needed parameters, doctors can obtain recommended antidiabetic medications in order of effectiveness and thus make their treatment judgment accordingly. The system enables physicians to save time in answering patients’ questions and can reduce the risk of developing clinical inertia.

5. Conclusion and Future Work

The prescription strategy of clinical doctors must take many factors into account. To address this, we developed an individualized antidiabetic medication recommendation system for patients with diabetes. This system, which can be manipulated with relative ease, tailors HbA1c levels to mitigate patients’ differences. Currently, 12 kinds of antidiabetic medications, both oral and injected, are available. Manually considering all possible conditions is not only a waste of medical resources but also a burden on the system, not to mention that it is impractical. This study, which combines fuzzy logic and ontology reasoning, proposes an antidiabetic medication recommendation system for patients with diabetes. It promotes a new concept of “patient-centered diabetes therapy.” Antidiabetic medications are recommended for the outpatients, and useful ranking of medications is conducted. In addition to aiding doctors’ clinical diagnosis, the system can not only serve as a guide for doctors specializing in diabetes but also help family practitioners and interns in prescribing medications.

Based on results of the study using the feedback system of operations, the seven factors analyzed can provide dynamic correlations. We will improve our system interface and dynamic weighting calculations in future research. Besides, we propose an architecture based on rules to build an antidiabetic medication recommendation system. In the future, we will combine rule-based and case-based reasoning to solve the special case issues.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

This work was supported by the Ministry of Science and Technology, Taiwan (Grant nos. 103-2632-E-324-001-MY3, MOST-104-2221-E-324-019-MY2, MOST-106-2221-E-324-025, and MOST-106-2218-E-324-002).

References

  1. Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century, The National Academies Press, Washington, DC, USA, 2001.
  2. C.-T. Bau, R.-C. Chen, and C.-Y. Huang, “Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning,” Telemedicine and e-Health, vol. 20, no. 5, pp. 460–472, 2014. View at: Publisher Site | Google Scholar
  3. F. J. Ampudia-Blasco, P. Y. Benhamou, G. Charpentier et al., “A decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes,” Diabetes Technology & Therapeutics, vol. 17, no. 3, pp. 194–202, 2014. View at: Publisher Site | Google Scholar
  4. C.-L. Hwang, Y.-J. Lai, and T.-Y. Liu, “A new approach for multiple objective decision making,” Computers & Operations Research, vol. 20, no. 8, pp. 889–899, 1993. View at: Publisher Site | Google Scholar
  5. R. F. Alharbi, J. Berri, and S. El-Masri, “Ontology based clinical decision support system for diabetes diagnostic,” in 2015 Science and Information Conference (SAI), pp. 597–602, London, UK, 2015. View at: Publisher Site | Google Scholar
  6. Stanford Center for Biomedical Informatics Research (BMIR), “Protégé,” June 2017, http://protege.stanford.edu. View at: Google Scholar
  7. R.-C. Chen, Y.-H. Huang, C.-T. Bau, and S.-M. Chen, “A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection,” Expert Systems with Applications, vol. 39, no. 4, pp. 3995–4006, 2012. View at: Publisher Site | Google Scholar
  8. P. Sharma and P. D. Kaur, “Effectiveness of web-based social sensing in health information dissemination—a review,” Telematics and Informatics, vol. 34, no. 1, pp. 194–219, 2017. View at: Publisher Site | Google Scholar
  9. Y.-F. Zhang, Y. Tian, T.-S. Zhou, K. Araki, and J.-S. Li, “Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems,” Computer Methods and Programs in Biomedicine, vol. 123, pp. 94–108, 2016. View at: Publisher Site | Google Scholar
  10. D. E. Forbes, P. Wongthongtham, J. Singh, and S. C. Thompson, “Ontology supported assistive communications in healthcare,” Communications of the Association for Information Systems, vol. 34, pp. 297–322, 2014. View at: Google Scholar
  11. P. C. Sherimon and R. Krishnan, “OntoDiabetic: an ontology-based clinical decision support system for diabetic patients,” Arabian Journal for Science and Engineering, vol. 41, no. 3, pp. 1145–1160, 2016. View at: Publisher Site | Google Scholar
  12. L. Marco-Ruiz, C. Pedrinaci, J. A. Maldonado, L. Panziera, R. Chen, and J. G. Bellika, “Publication, discovery and interoperability of clinical decision support systems: a linked data approach,” Journal of Biomedical Informatics, vol. 62, pp. 243–264, 2016. View at: Publisher Site | Google Scholar
  13. S. E. Inzucchi, R. M. Bergenstal, J. B. Buse et al., “Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of diabetes (EASD),” Diabetes Care, vol. 35, no. 6, pp. 1364–1379, 2012. View at: Publisher Site | Google Scholar
  14. S. E. Inzucchi, R. M. Bergenstal, J. B. Buse et al., “Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes,” Diabetes Care, vol. 38, no. 1, pp. 140–149, 2015. View at: Publisher Site | Google Scholar
  15. C.-H. Chang, Y.-D. Jiang, C.-H. Chung, L.-T. Ho, and L.-M. Chuang, “National trends in anti-diabetic treatment in Taiwan, 2000–2009,” Journal of the Formosan Medical Association, vol. 111, no. 11, pp. 617–624, 2012. View at: Publisher Site | Google Scholar
  16. H.-T. Ou, K.-C. Chang, Y.-M. Liu, and J.-S. Wu, “Recent trends in the use of antidiabetic medications from 2008 to 2013: a nation-wide population-based study from Taiwan,” Journal of Diabetes, vol. 9, no. 3, pp. 256–266, 2016. View at: Publisher Site | Google Scholar
  17. T. Kohro, T. Yamazaki, H. Sato et al., “Trends in antidiabetic prescription patterns in Japan from 2005 to 2011,” International Heart Journal, vol. 54, no. 2, pp. 93–97, 2013. View at: Publisher Site | Google Scholar
  18. C. Rafaniello, V. Arcoraci, C. Ferrajolo et al., “Trends in the prescription of antidiabetic medications from 2009 to 2012 in a general practice of Southern Italy: a population-based study,” Diabetes Research and Clinical Practice, vol. 108, no. 1, pp. 157–163, 2015. View at: Publisher Site | Google Scholar
  19. S.-H. Ko, D.-J. Kim, J.-H. Park et al., “Trends of antidiabetic drug use in adult type 2 diabetes in Korea in 2002–2013: nationwide population-based cohort study,” Medicine, vol. 95, no. 27, article e4018, 2016. View at: Publisher Site | Google Scholar
  20. A. S. Abdelmoneim, D. T. Eurich, J.-M. Gamble, and S. H. Simpson, “Use patterns of antidiabetic regimens by patients with type 2 diabetes,” Canadian Journal of Diabetes, vol. 37, no. 6, pp. 394–400, 2013. View at: Publisher Site | Google Scholar
  21. J. C. Hsu, D. Ross-Degnan, A. K. Wagner et al., “Utilization of oral antidiabetic medications in Taiwan following strategies to promote access to medicines for chronic diseases in community pharmacies,” Journal of Pharmaceutical Policy and Practice, vol. 8, no. 1, p. 15, 2015. View at: Publisher Site | Google Scholar
  22. R.-C. Chen, C.-Y. Huang, C.-T. Bau, and L.-S. Chen, “A decision support system for diabetes medicine selection using patient centered treatment based on fuzzy logic and domain ontology,” International Journal of Innovative Computing, Information and Control, vol. 13, no. 5, pp. 1681–1692, 2017. View at: Google Scholar
  23. A. J. Garber, M. J. Abrahamson, J. I. Barzilay et al., “Consensus statement by the American Association of Clinical Endocrinologists and American College of endocrinology on the comprehensive type 2 diabetes management algorithm – 2016 executive summary,” Endocrine Practice, vol. 22, no. 1, pp. 84–113, 2016. View at: Publisher Site | Google Scholar
  24. Y. Handelsman, Z. T. Bloomgarden, G. Grunberger et al., “American Association of Clinical Endocrinologists and American College of Endocrinology - clinical practice guidelines for developing a diabetes mellitus comprehensive care plan - 2015,” Endocrine Practice, vol. 21, Supplement 1, pp. 1–87, 2015. View at: Publisher Site | Google Scholar
  25. J. Rada-Vilela, “FuzzyLite: a fuzzy logic control library,” June 2017, http://www.fuzzylite.com. View at: Google Scholar
  26. Stanford Center for Biomedical Informatics Research (BMIR), “WebProtégé,” June 2017, http://protege.stanford.edu/products.php#web-protege. View at: Google Scholar
  27. Apache Software Foundation, “Apache Jena,” June 2017, http://jena.apache.org. View at: Google Scholar
  28. H. Rodbard, P. Jellinger, J. Davidson et al., “Statement by an American Association of Clinical Endocrinologists/American College of Endocrinology Consensus Panel on type 2 diabetes mellitus: an algorithm for glycemic control,” Endocrine Practice, vol. 15, no. 6, pp. 540–559, 2009. View at: Publisher Site | Google Scholar
  29. A. J. Garber, M. J. Abrahamson, J. I. Barzilay et al., “AACE/ACE Comprehensive diabetes management algorithm 2015,” Endocrine Practice, vol. 21, no. 4, pp. 438–447, 2015. View at: Publisher Site | Google Scholar
  30. Y. Handelsman, Z. T. Bloomgarden, G. Grunberger et al., “American Association of Clinical Endocrinologists and American College of Endocrinology - clinical practice guidelines for developing a diabetes mellitus comprehensive care plan - 2015 - executive summary,” Endocrine Practice, vol. 21, no. 4, pp. 413–437, 2015. View at: Google Scholar
  31. A. Garber, M. Abrahamson, J. Barzilay et al., “American Association of Clinical Endocrinologists’ comprehensive diabetes management algorithm 2013 consensus statement,” Endocrine Practice, vol. 19, Supplement 2, pp. 1–48, 2013. View at: Publisher Site | Google Scholar
  32. C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, vol. 186, Springer-Verlag, Berlin, Heidelberg, 1981.
  33. Wikipedia, “TOPSIS,” June 2017, https://en.wikipedia.org/wiki/TOPSIS. View at: Google Scholar
  34. S.-S. Chang, Fuzzy Multi-Criteria Decision Making for Evaluation Method, Wu-Nan Book Inc., Taiwan, 2012.

Copyright © 2017 Rung-Ching Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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