A Knowledge-Based Approach for Evaluating Impact of Therapeutic

Strategies

Nadia Abchiche-Mimouni

1 a

, Mike Donald Tapi Nzali

2 b

and Franc¸ois Gueyfﬁer

3 c

Laboratoire IBISC, Univ. Evry, Universit

e Paris-Saclay, France

LIRMM, Department of Computer Science, Montpellier University, Montpellier, France

LBBE, Universit

e Claude Bernard Lyon 1, France

ﬁ

Keywords:

Rule-Base Systems, Simulation in Healthcare.

Abstract:

This paper proposes an original approach for modelling medical expertise and simulating medical strategies. A

knowledge-based system is used to model therapeutic strategies according to three axes: diagnostic, prescrip-

tion and treatment effect. The diagnostic axis describes the ways of deciding whether an individual is eligible

for treatment or not. The prescription axis models the ways of choosing an adequate drug for an individual

or changing the current treatment if it is judged ineffective. Treatment effect concerns the effect of a drug at

the individual level. This modelling is used for exploring different therapeutic strategies and quantifying their

impact on the individual and population levels. We have developed a platform, based on a rule-based system,

that was validated with a Use-case in Hypertension management. Classical and Alternative strategies have

been simulated with the same Realistic virtual population. 20.000 individuals were considered and several

parameters (e.g. optimal drug prescription, evolution of the cardiovascular risk) were calculated. The experi-

ments showed the viability and relevance of the approach. Its strengths are numerous. Since the rules are the

input of the system, they can be introduced and modiﬁed by non-programmers people, allowing prescribers to

fully test their own rules. The platform is conﬁgurable in terms of modelled expertise and in terms of outputs

to be measured. Empirical results concerning the superiority of the Alternative strategies have been produced.

1 INTRODUCTION

Evidence-Based Medicine (EBM) is described as “the

conscientious, explicit, and judicious use of current

best evidence in making decisions about the care

of individual patients” (Sackett et al., 1996). From

the perspective of public health workers and policy-

makers (Sheridan and Julian, 2016), it is more about

having evidence or data on the impact of diagnosis

and treatment at the population level. In this case,

the impact of the cost factor is decisive. Clinicians

need to access to different kinds of knowldege to take

the decision which is the most adequate according

to the patient’s state of health, environment and

comorbidities. The patient’s individual preferences

are also very important in order to ensure adherence

to treatment.

https://orcid.org/0000-0001-6149-3345

https://orcid.org/0000-0002-6245-5516

https://orcid.org/0000-0002-9921-0977

This paper addresses an important challenge that

EBM raised, that is providing means and/or tools for

evidence-based decision making. Tools for public

health workers and policy makers are particularly

targeted. This allows physicians or policy-makers to

formulate clinical recommendations and guidelines

on the principles of EBM. Sillico modelling methods

have proven to be very helpful while exploring

(whilst more cheaply) the impact of therapeutic

strategies. This concerns as well the individual

level ((Gao, 2019), (Troche et al., 2000)), where

the aim is to provide a personnalized prescription,

as well the population level ((Patel et al., 2021),

(Kotecha et al., 2021)). In the later case, the aim is

to consider collective beneﬁts of the population in

terms of efﬁciency and cost. In both approaches,

balancing risk and beneﬁt is an important question.

The existing approaches are based on meta-analysis

(Gao, 2019), randomized clinical (Duffy J and RJ,

2017), and/or on speciﬁc mathematical modelling

((Cottura et al., 2020), (Gumel et al., 2002)). Most

of them are disease-speciﬁc or dedicated to sub-

1036

Abchiche-Mimouni, N., Nzali, M. and Gueyfﬁer, F.

A Knowledge-Based Approach for Evaluating Impact of Therapeutic Strategies.

DOI: 10.5220/0011894300003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 3, pages 1036-1046

ISBN: 978-989-758-623-1; ISSN: 2184-433X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

problems of a disease. From another perspective,

since the ﬁrst well known medical expert system

MYCIN, developed by Shortliffe (Shortliffe, 1976)

(to help physicians when prescribing antimicrobial

drugs) medical applications in Artiﬁcial Intelligence

(AI) have been continually developed. AI has been

extensively employed in medicine by modelling

expertise thanks to Knowledge-Based systems and/or

by treating medical datasets with Machine Learning

algorithms (Nayyar et al., 2021). However, KBS AI

methods have not been used for the evaluation of

therapeutic strategies. Yet, healthcare applications

are ”knowledge-intensive” (Schreiber et al., 1999)

i.e, knowledge and expertise play a key role.

The present work ﬁlls this gap by proposing a

knowledge-based platform for modelling and simu-

lating therapeutic strategies with a virtual population.

Such a platform is proposed to help physicians and

policy makers to respectively formulate individual-

ized clinical decisions and, recommendations and

guidelines on the principles of EBM. The simulation

allows to illustrate and quantify the beneﬁts and/or

the impact of a therapeutic strategy. Moreover, it

allows to compare different therapeutic strategies.

The approach has been designed in collaboration

with physicians who helped during the modelling

process of the therapeutic strategies and during

the experiments. It has been instantiated with an

application in Hypertension management, where two

therapeutic strategies and a given hypertensive virtual

population are used. The ﬁrst therapeutic strategy

is the one which is consistent with the European

ofﬁcial guidelines for Hypertension management

((Graham and AL., 2007), (Fagard and AL., 2013),

(Unger et al., 2020)). Such a strategy is generally

in accordance with what is -supposed- to be put

into practice by physicians. It is named as Classical

strategy. An Alternative strategy is able to consider

alternative factors (risk, age, sex...) either for the

diagnostic or the prescription.

In the present work, two therapeutic strategies

(Classical and Alternative) for Hypertension manage-

ment have been simulated and evaluated, allowing

to quantify their beneﬁts. The gain is measured for

each strategy (e.g. in terms of blood pressure level

decreasing, number of treated individuals), therefore

enabling the quantiﬁcation of the impact in terms

of public health. The use of a virtual population

allows one to consider different assumptions, through

different settings, on large sizes of data cheaply and

safely (Richard J. Chen, 2021),(Ivanny, 2018). The

knowledge describing the strategies have been mod-

elled by means of production rules in order to make

it possible to modify the strategies in a declarative

way, i.e., without modifying the application program.

Because it does not require advanced programming

action, the physicians are able to introduce the

strategies in the system by themselves. Physicians

can also modify the implemented strategies and the

parameters of the simulation.

The next section of this paper presents the nec-

essary background for modelling Hypertension man-

agement. Section 3 describes the material and meth-

ods. Section 4 presents a Use-case showing the us-

ability and preleminary results of the approach in Hy-

pertension management. Section 5 provides a discus-

sion according to the contributions and related work.

Finally, the conclusion summarizes the contribution

of this paper and proposes some improvements and

perspectives for the future.

2 KNOWLEDGE MODELING

The considered domain application is related to Hy-

pertension management. This is a major risk factor

behind cardiovascular diseases, which is the largest

causes of death in the world (Roth and al., 2020).

For this reason, it is well studied and documented,

resulting in a large source of expertise and data. The

availability of home kits for self-measurement allows

for collecting patients data easily. Ofﬁcial guidelines

(Fagard and AL., 2013) offer clinicians guidance on

Hypertension management using the best scientiﬁc

evidence. Clinicians acquire some expertise from

their everyday practice which is also important.

Other sources of knowledge derived from physiology

or medical genetics may also be available

Different levels of knowledge representation can

be considered while describing complex domain

knowledge. The most popular is the one proposed

in Richter (59), based on three different levels (see

Figure 1). The ﬁrst level is the Cognitive level. It

contains the expression of the expertise in human and

informal way. The second is the Representation level

which implies the use of formal (logical) languages

for expressing the knowledge. The third level is

the Implementation level, which is machine-oriented

(uses data structures and programs). As shown in Fig-

ure 1, there is a hierarchical relationship between the

three levels, meaning that the Cognitive level requires

It is well known that Hypertension is often associated

with other diseases. But, for simpliﬁcation reasons, this will

not be considered in this work.

A Knowledge-Based Approach for Evaluating Impact of Therapeutic Strategies

1037

For young persons, consider BB ﬁrst

...

IF (age < 50) THEN Choose BB

...

(defrule RR(< age 50)=>(assert (Treatment BB)))

...

Figure 1: Three-levels modelling.

a translation to be expressed into the Representation

level, which itself is translated into Implementation

level. Other proposed methodologies (e.g. (Schreiber

et al., 1999)) are more sophisticated, in terms of rep-

resentation levels and modelled concepts. To simplify

the discussions with the experts, this three-level mod-

elling method has been adopted. As it will be shown

in Section 3, the Cognitive level is used to express a

set of therapeutic strategies a in Hypertension domain

in a form close to natural language. The Representa-

tion and the Implementation levels are used to formal-

ize these knowledge. Figure 1 shows an example that

expresses the fact that the physicians use in general

Beta Blokers drugs for young poeple.

2.1 Therapeutic Strategies

A Therapeutic Strategy (TS) is deﬁned according to

three dimensions: Diagnostic, Prescription and treat-

ment Effect (DPE). Each dimension represents a par-

Figure 2: DPE Cycle visit.

ticular step of the global therapeutic process. Figure 2

shows the DPE cycle which is associated to a medical

visit (Visit

2.1.1 Diagnostic

It is the way of deciding whether an individual is el-

igible for treatment (i.e., the individual is considered

to be hypertensive) or not. For example, a Classical

strategy consists in considering only the Blood Pres-

sure (BP) level to classify an individual as hyperten-

sive. This is generally decided based on several visits

to the doctor. An average of the measures is calcu-

lated and used for the decision. Instead of relying

solely on the pressure level of BP for deciding that

an individual is hypertensive, it also considers indi-

vidual factors such as the level of risk of developing a

stroke. The study of the individual’s proﬁle as to the

evolution of the BP and the habits of the person, may

also provide elements of individualization of the diag-

nosis. Moreover, it is already known that the age and

sex can be taken into account ((Osude et al., 2021),

(Schoepﬂin et al., 2021)).

2.1.2 Prescription

It consists in choosing an adequate drug for a hyper-

tensive individual or in changing the current treatment

if it is judged ineffective. The way of choosing the

drug can be based on some predeﬁned criteria (e.g.

gender, age). The Classical strategy considers that the

treatments have different effects due to unknown rea-

sons. So, the choice of a treatment can be assimilated

as being random. As for the Diagnostic, an Alterna-

tive strategy considers different parameters (e.g. risk)

for choosing a drug. It is also possible to combine

drugs.

2.1.3 Treatment Effect

Not much is known about the prediction of effect

of hypertensive drug on BP that would be patient

speciﬁc. We used the results from the IDEAL trial

(Gueyfﬁer et al., 2015), completing those from

the Dickinson’s study (Dickerson et al., 1999),

supporting that BP reduction is positively corre-

lated with the age for Diuretics (DI) and Calcium

Antagonists (CA), and negatively correlated with

the age for Beta Blockers (BB) and, in general, for

Angiotensin-Converting Enzyme Inhibitors (ACEI).

Beyond the link with age, the residual variability

has been considered as random noise. In order to

model the beneﬁt of a drug, expressions (1) and

(2) were used to quantify the treatment effect at

the individual level. The expressions 1 and 2 have

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

1038

been inspired from obtained results in (Gueyfﬁer

et al., 2015) concerning the AC inhibitors and the

DI. The extrapolation to CA and BB is justiﬁed by

the correlations observed in (Dickerson et al., 1999).

The new BP is calculated differently depending on

the group of the drugs and the age of the individual.

Indeed, the drugs are classiﬁed into two groups: (1)

DI, CA, and (2) BB, ACEI, and Sartans (SAR). It is

known that SAR have similar effect than ACEI and

BB. The ﬁrst group is more efﬁcient for aged persons,

with an effect increasing from 3 to 5% of BP with

age, and the second one is better for young persons

with an effect decreasing from 5 to 3% with age.

The age of the individuals of the population is

greater than 35 and less than 65 years. For the ﬁrst

group, the expression is:

F = 0.03 + 0.033 ∗(age − 35) ∗ 0.02 (1)

For the second group, the expression is:

F = 0.03 + 0.033 ∗ (65 − age) ∗ 0.02 (2)

The effect of the treatment E is calculated with: F +r,

where r is a randomly generated number with a spe-

ciﬁc normal distribution. To deﬁne a general TS, it is

sufﬁcient to specify each of the three dimensions de-

scribed above. onsidering particular criteria will fo-

cus on a particular strategy. For that purpose, it is

possible to rely on knowledge extracted from recent

scientiﬁc literature or on empirical acquired knowl-

edge by physicians during their hospital practices. In

this work, we relied on the expertise of the doctors we

partnered with.

3 MATERIAL AND METHODS

The aim of the proposed approach is to provide a

knowledge-based platform for simulating and eval-

uating TSs on a Realistic Virtual Population (RVP).

The simulation consists in launching a number of vis-

its for each individual accordding to the DPE cycle

(see Figure 2).

3.1 Virtual Population

Because of the difﬁculty in accessing a real dataset,

due to ethical and ﬁnancial considerations, synthetic

data are being increasingly used ((Schoepﬂin et al.,

2021), (Richard J. Chen, 2021)). In our case, syn-

thetic data were built based on ofﬁcial French demo-

graphic statistics and summarized data from represen-

tative observational studies. For that purpose, we used

the algorithm that had been developed in (Ivanny,

2018). This was used in (T. and A., 2002) to gener-

ate a hypothetical population that reproduced a gen-

eral practitioner patient list to test the cost effective-

ness of different screening strategies. The Algorithm

(see Figure 3) allows generating a RVP of a given

number of indivuals. The individuals have the same

Realis!c Virtual Popula!on

Figure 3: Realistic Virtual Population Algorithm. genera-

tion.

age, sex and cardiovascular risk factors proﬁle as the

French population aged 35-65 years. For each indi-

vidual, an identiﬁer is automatically assigned. The re-

maining individual characteristics are: sex, age, sys-

tolic arterial pressure, diastolic arterial pressure, total

cholesterol, hdl cholesterol, blood sugar, smoking or

non-smoking, and diabetes mellitus. The algorithm

is based on a specifc variance-covariance matrix. We

used it in the proposed approach to generate an RVP

for simulating different TSs. The approach has been

validated with Hypertension application. When dif-

ferent TSs are simulated, their efﬁciency can be eval-

uated in terms of BP evolving, the evolution of the

number of treated individuals, the distribution of pre-

scription drugs and, the rate of reducing the risk.

Moreover, the public health impact of employing a

particular strategy can be quantiﬁed depending, on,

for example, of number of treated individuals. Com-

paring the outputs of the TSs allows quantifying and

comparing their beneﬁts. The platform is based on

the architecture showed in Figure 4. It is composed

A Knowledge-Based Approach for Evaluating Impact of Therapeutic Strategies

1039

Figure 4: Platform Architecture.

of a rule-base that models the TSs and a Fact-base

for representing the RVP. The User Interface allows

setting the different parameters and conﬁgurations of

the application (e.g. the number of visits planned for

each individual composing the VRP, the thresholds

for BP). The Controller is in charge of controlling

a DPE cycle visit for each individual of the RVP. It

also treats the requests coming from the User Inter-

face. The Inference Engine triggers the rules (from

TSs rule-base) according to their relevance as deﬁned

by the Controller. Each of these components is de-

scribed hereafter.

3.2 TSs Rule-Base

For structuring the TSs, we have opted for the pro-

duction rules formalism. This choice is motivated by

different reasons. It is close to the expert reasoning

which is often in the ”IF-THEN” form. Additionaly,

it is expressed in a textual form that is easier to

understand. So, it is easy for the experts to adhere to

such a formalism. Adding or modifying a production

rule simply consists of adding production rules or

overwriting the existing ones. The processing and

control of the rules are independent of the rules

themselves. So, the same Inference Engine is used

to process different Rule-bases. The user does not

need to know about how the system triggers nor

compiles the rules. The most common rules form is

the IF Conditions T HEN Action one, meaning that

if Conditions are satisﬁed then Action is executed.

Other types of rules like the m o f n rules where,

given n Conditions, the Action is executed if m

conditions from n (m ≤ n) are satisﬁed. Also, the

rules can be labeled with a conﬁdence degree or a

strength.

For modelling the TSs, the rules are grouped

in packages depending on which phase of the DPE

Cycle they are part of. Their general form is:

PR: i f (pr

) (pr

) ... (pr

) ...and (pr

)

then Action, where:

PR is the name of the production rule and,

(1 <= i <= n) is a premise that expresses a pa-

rameter constraint, pr

= (x

op α

), where x

is the

name of the i

parameter, α

is its value, and op ∈

{<, >, =, <=, >=} is an operator comparison. Ex-

amples of simpliﬁed rules are shown in Tables 1. The

Diagnostic rule (a) is used to decide whether the indi-

vidual is hypertensive if his/her Systolic Blood Pres-

sure (SBP) is higher than 140. The prescription rule

(b) chooses the BB drug if the individual is under 50

years old. The Effect rule (c) calculates the drug ef-

fect for a treated individual who is over 50 years old.

Table 1: Examples of production rules.

i f (PAS > 140) then (Hypertensive True) (1)

i f (Hypertensive True)(age < 50)

then (Drug BB)(Treaded True) (2)

i f (Treated True)(age > 50)

then (Calculate − BP post −treat) (3)

3.3 RVP Fact-Base

The RVP is a set of individuals where a list of char-

acteristics is instantiated for each individual. Each

individual is represented as a Fact-base in the form:

) (c

) ... (c

), where: n is the

number of characteristics and, v

is the value of the

characteristic named c

. Table 2 shows examples.

Table 2: Examples of facts from RVP base.

(id 1) (sexe 1) (age 43.60) (pas 111.09)

(pad 70.87)(cto 1.96)(hdl 0.45) (glyc 1.17)

(dm 79.19)(tab 60.16) (hvg 172.75)

(id 2) (sexe 2) (age 43.38) (pas 142.80)

(pad 98.362)(cto 2.34)(hdl 0.29) (glyc 0.90)

(dm 87.75)(tab 72.63) (hvg 167.76)

(id 3) (sexe 1) (age 37.0) (pas 125.60)

(pad 80.57)(cto 2.22)(hdl 0.36) (glyc 0.740)

(dm 72.01)(tab 55.71) (hvg 169.92)

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

1040

3.4 User Interface

The User Interface allows for adjusting the simula-

tion settings, such as the parameters deﬁning a strat-

egy, the virtual population to employ (by uploading

associated text ﬁles) and the desired parameters to be

extracted as outputs. It is dedicated to the interaction

between the system (Controller component) and the

user. Its usefulness is threefold:

1. loading the TSs and the RVP, with adequate pa-

rameter values.

2. launching the results to extract from the simula-

tion.

3. displaying the results (curves, histograms).

Here is a parameter shortlist which can be set for each

execution session: the number of visits, the threshold

values for SBP, DBP, and risk, and, the equation coef-

ﬁcients for calculating the treatment effect. Here are

some outputs examples:

• Histograms for measuring the optimal drug.

• Curves for measuring the BP evolution.

• The diagnostic rate which represents the numbers

of individuals considered hypertensive divided on

the number of individuals of the RVP.

• The success rate is the ratio between the number

of individuals on treatment for whom the BP is

under control, and the number of individuals in

treatment.

3.5 Controller

The Controller is in charge of two primary tasks. The

ﬁrst task consists of launching the number of visits

for each individual of the RVP, and monitoring their

execution according to the DPE cycle. The Controller

acts as a metalevel by giving a focus to the Inference

Engine. The focus is successively on the Diagnosis

rules package, then the Treatment rules package, and

ﬁnally, the Effect rules package. Thus, all individuals

are simulated. The second task launches the calcula-

tions of the TSs results needed by the user. The two

tasks are performed through production rules. For that

purpose, the Controller adds adequate facts in the Lo-

cal Memory making it possible to trigger the appro-

priate rules.

3.6 Local Memory

The Local Memory contains the facts and the rules

used by the User Interface and the Controller. The

rule R1 is an example of a production rule, from

Local Memory, that expresses that it is to launch the

Diagnostic phase of the DPE cycle.

R1: i f (DPE Diag)(Individual ?p) then

(process diag ?p)

The fact F1 (respectively F2) is a fact, from Local

Memory, that expresses that it is to calculate the

success rate (respectively to switch to the Diagnostic

step of DPE Cycle):

F1: (calculate success rate), F2: (DPE Diag).

3.7 Inference Engine

The Inference Engine is the core component of the

platform. It triggers the rules, allowing their actions

to be executed. The Action part of a rule consists of

adding new facts in the working memory or the exe-

cution of a function. This provides great ﬂexibility in

what can be done with such rules. This is the reason

why the Inference Engine component is used for all

the automated processes of the platform. For the

implementation purpose, we have used CLIPS

lan-

guage, which is a rule-based programming language

and scripting environment. Such a language is useful

for creating Expert Systems and other programs

where a heuristic solution is easier to implement

than an algorithmic solution. CLIPS provides a list

of functions that can be executed in the Action part

of the rules. It also allows deﬁning functions for

application needs. The CLIPS formalism is based

on Propositional Logic and First Order Predicate

Calculus. A CLIPS knowledge base is composed of

facts which are true or false, and rules, which are

triggered according of their relevance by an instance

the CLIPS engine. Any proposition (as they are used

in Propositional or ﬁrst order Logic) can be repre-

sented as a CLIPS fact. For instance, an individual

whose Id is 35, and SBP is expressed as 145mmHG,

the two facts (SBP 145) and (Id 35) are asserted.

But, if we want to deal with different person’s SBP,

one can use a structure (Individual) with two slots

(Id and SBP) that describe the Individual entity. The

facts become: (Individual (Id 35)(SBP 145)). This

corresponds very well with the familiar concept of

an object with its attributes. The facts (SBP 145)

and (Id 35) are represented as ordered facts whereas

the proposition (Individual (Id 35)(SBP 145)) is a

compound proposition, which is more conveniently

represented by an unordered fact.

http://www.clipsrules.net/, accessed on January 11,

2023.

A Knowledge-Based Approach for Evaluating Impact of Therapeutic Strategies

1041

The simple rule R1 should be triggered if any

patient has a SBP equal to 145.

(de f rule R1 (Individual (Id ?x)(SBP 145)) =>

(printout ?x has a high Systolic Blood Pressure))

We can also write the rule R2 that deals with a

false proposition: (de f rule R2 (not (SBP 145)) =>

(printout SBP value is not 145 !))

Obviously, to express the TSs, we need more

complex rules expressions. Let’s take the following

sample from ofﬁcial guidelines (Graham and AL.,

2007) for Hypertension management: ”The decision

to start hypertensive treatment should be based on

two criteria, the level of systolic being greater than

140 and diastolic blood pressure greater than 90”.

The translation of this guideline into a CLIPS rule

(R3) could be as follows:

(de f rule R3

(Individual (Id ?id)(> SBP 140)(> DBP 90)

(> NB Mesures 3)

=> (Hypertensive ?id Yes))

The individuals of the RVP are automatically

compiled into facts in the CLIPS syntax. This has

been done thanks to a script written in AWK lan-

guage

. In Figure 4, the oval red dotted line form

regroups the components of the architecture that are

related to the Structure level. The oval green dotted

line form regroups the components of the Implemen-

tation level. As shown in Figure 4, the Inference En-

gine is at the junction between the two levels. To show

the feasibility and the usefulness of the approach, the

present work focused on a sub-set of Hypertension

knowledge.

4 USE-CASE

Our Use-case is related to the management of Hyper-

tension and associated cardiovascular risk. We chose

a small subset of european guidelines ((Graham and

AL., 2007), (Fagard and AL., 2013)). Two simple

TSs were deﬁned to conduct the experiment of the

approach with a RVP such as described in section 3.1.

The two strategies, a Classical strategy and Aternative

strategy, were implemented. Their respective results

were quantiﬁed to evaluate the beneﬁt of each one in

terms of optimal drug prescription, number of avoided

https://www.gnu.org/software/gawk/manual/, ac-

cessed on January 11, 2023.

cardiovascular accidents, decreasing BP levels, num-

ber of treated individuals, diagnosis rate success (e.g.

successful treatment ) and more generally, their suc-

cess rate (that can include elements such as the suc-

cessful diagnostic).

4.1 Therapeutic Strategy Setting

Each TS is deﬁned by specifying each of the three

rules packages: (1) Diagnostic rules, (2) Prescription

rules and (3) Treatment Effect rules. Launching a

strategy consists of parametrizing the User Interface

items by changing the default value(s) assigned to

each parameter. The Classical strategy is based on the

BP threshold for the diagnosis. For the prescription,

it acts in a random way for choosing a drug, based

on the hypothesis that the prescriber believes that all

the drugs are similar and produce different effects for

different individuals without any known reasons. The

Alternative strategy considers the BP threshold, age,

gender, and cardiovascular risk when choosing an ad-

equate drug. For the prescription, it assumes that the

prescriber believes that the effect of the treatment de-

pends on individual parameters. Some drugs (ACEI,

SAR, BB) are more efﬁcient for young people, and

some others (DI, CA) are more suitable for people

over 50. The same Treatment Effect rules were used

in order to be able to quantify the contrast between

the two strategies. The two strategies were simulated

with the same RVP.

4.2 Experimentation

A same RVP of 20.000 individuals was used to con-

duct the simulation of each strategy. This size is easy

to modiﬁy since the RVP is automatically generated

thanks to the algorithm described in (Figure 3). For

each strategy, ten visits were planned for each indi-

vidual of the RVP. At each visit, the number of suc-

ceeded treatments and the mean of the measured BP

were calculated. At the end of the simulation, we plot-

ted for each strategy, the curve representing the evo-

lution of the BP (Figure 5), the histograms represent-

ing the number of prescriptions (Figure 6 and 7), the

number of treated individuals at each visit (Table 3)

and, the absolute risk at each visit (Table 4). We eval-

uated the gain when ofﬁcial recommendations (Clas-

sical strategy) are respected. The conducted experi-

Table 3: Number of treated individuals.

Strategy v4 v6 v8 v10

Alternative 2407 2637 2836 2953

Classical 2397 2657 2626 2998

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

1042

ments showed how we can quantify the efﬁciency of

a particular strategy according to selected parameter

values. The curves of Figure 5 represent the evolu-

tion of the mean of measured BP over the time for

individuals which are under treatment for each strat-

egy. The experiments were also used to quantify the

Classical strategy

Alterna!ve strategy

130

136

140

146

160

0 2 4 6 8 10

Average measured SBP

Number of treated individuals/visit

Figure 5: Blood presure evolving.

superiority of one of the two strategies under deﬁned

hypothesis. Throughout the visits, a pressure differ-

ence of 1.5 mmHg is observed; showing that the al-

ternative strategy is slightly better than the classical

one. Indeed, the average BP of the population after

ten visits is greater for the Classical strategy than for

the Alternative strategy. Table 3 visualizes the num-

ber of treated individuals at each visit, allowing to

know the number of sick individuals in the RVP. Un-

like the diagnostic method based solely on BP, we see

that fewer patients were treated when the diagnosis

considers the risk. When the treatment is based on

BP, the goal is twofold: to reduce the BP and the risk

of an accident. Considering the reduction of the risk

of accidents at each visit makes it possible to visual-

ize the number of avoided accidents. Table 4 shows

that the risk is lower with the Alternative strategy (A),

than the Classicla one (C) meaning that the number of

avoided accidents is higher. Another way to quantify

the efﬁciency of the strategies is the way of choosing a

treatment. More precisely, it is the ability of a strategy

to identify the optimal treatment for each individual.

The optimal treatment is the one that gives the high-

Table 4: Absolute risk at each visit.

Strategy v5 v6 v7 v8 v9

A 0.022 0.026 0.021 0.015 0.013

C 0.0195 0.0194 0.0193 0.0194 0.0195

Drug

AC BB DI ACEI SAR

Frequency

100

200

300

400

500

600

700

800

900

1 000

1 100

1 200

1 300

Figure 6: Optimal treatments for people over 50 years with

Classical strategy.

Frequency

Drug

AC BB DI ACEI SAR

250

500

750

1 000

1 250

1 500

1 750

2 000

2 250

Figure 7: Optimal treatments for people over 50 years with

Alternative strategy.

est BP decrease during the visits. Figure 6 shows the

results of optimal treatments for individuals over 50

years during the simulation of the Classical strategy,

while Figure 7 shows the results of optimal treatments

for individuals over 50 years during the simulation of

the Alternative strategy.

We note the dominance of AC and DI drugs in

both strategies, but it is much clear in the Alternative

strategy. The dominance observed in the Alternative

strategy is, obviously, due to the fact that it is based on

an age-appropriate prescription. However, it is impor-

tant to reiterate that the simulation is neither used for

optimizing the treatment (such as it is done in (Ben-

nett and Hauser, 2013)) nor for discovering new fea-

tures of Hypertension or its treatment. The aim of the

simulation is to:

1. Illustrate the consequences of considering some

scientiﬁc facts, ignored in the current guidelines.

2. Estimate the extent of BP control improvement

through the application of new rules.

A Knowledge-Based Approach for Evaluating Impact of Therapeutic Strategies

1043

5 DISCUSSION

Thanks to the developed platform, a Classical and an

Alternative strategies were simulated with a Realis-

tic virtual population. Then, their results were an-

alyzed and compared. The conducted experiments

showed how we can quantify the efﬁciency of a par-

ticular therapeutic strategy according to selected pa-

rameters. The originality and the strengths of our

approach are numerous. The prescription, diagnos-

tic and drug effect rules are the input of the sys-

tem. These rules can be introduced and modiﬁed

by non-programmers people, allowing prescribers to

fully test their own rules. This is due to the fact that

the AWK script (used to compile the RVP) and, the

production rules are an inputs of the platform. The

structure of the facts can be modiﬁed by adapting

the script. Moreover, the expertise should be writ-

ten in XML language (CLIPS supports it), so it is

not necessary for the user to know about the syn-

tax of CLIPS rules and facts in order to proceed to

any modiﬁcation. This allows adding new parame-

ters with new rules that will process these parame-

ters. Since all the expertise is written in production

rules, the platform can be applied to any other appli-

cation domain. Our approach provides a complement

to the statistical tools that often impose a rigid data

format and manipulation requiring the mastery of the

tool. In fact, the proposed platform used production

rules for modelling the therapeutic process, which is

close to human deduction reasoning, as employed by

physicians. Note that our platform allowed differents

kinds of experimentations that have not been reported

here. In this paper, we reported only a part of the

carried ones. The source code of the platform, de-

tails about how to parametrize an experimentation,

and a sample of the used data and expertise can be

found on: https://github.com/nabchiche/ISThMe.git.

To the best of our knowledge, this work is the ﬁrst

that proposes an approach that combines such ad-

vantages. Existing approches are based on meta-

analysis (Gao, 2019), randomized clinical (Duffy J

and RJ, 2017), and/or on speciﬁc mathematical mod-

elling ((Cottura et al., 2020), (Gumel et al., 2002)).

Most of them are disease-speciﬁc or dedicated to sub-

problems of a disease. More recently, Machine Lean-

ing has gained in interest due to their prediction power

(Phan et al., 2022). Unfortunately, they suffer from

the Blax-box effect that make them unusable, espe-

cially when evidence is needed to take a decision,

such as in medicine.

6 CONCLUSION AND

PERSPECTIVE

We have presented an approach for studying and eval-

uating the efﬁciency of therapeutic strategies in Hy-

pertension management. The approach proposed a

platform where the knowledge describing the thera-

petic strategies had been modelled by means of pro-

duction rules. This makes it possible to modify the

strategies in a declarative way, i.e., without modify-

ing the source code of the application.

Current work concerns the addition of more com-

plex expertise so that one can deal with comorbidi-

ties. Hypertension is often associated with other dis-

eases (e.g., diabetes). Comorbidities can be consid-

ered by implementing the ability to use different ef-

fect models of drugs that consider comorbidity fac-

tors (e.g. by considering kinetic models ((Holford

and Sheiner, 1982), (Donnelly et al., 1992)). More-

over, it should be possible to consider the variability

of the BP according to the day-night rhythm or the

seasons ((Giles, 2006), (Chen and Yang, 2015)). In

the current version of the system, the knowledge base

related to the ofﬁcial guidelines had been manually

extracted. An open direction that would seem natu-

ral is to compile the expertise into rules and facts di-

rectly usable by CLIPS; so, the human intervention

would be minimal. For that purpose, the work done

in (Isern et al., 2007) could be a precious source of

inspiration. The authors provide a ﬂexible framework

to follow the execution of clinical guidelines based

on an ontology and a multi-agent system. Another

idea to explore is the use text of mining techniques

for buildings production rules from clinical guide-

lines. The proposed approach would be very helpful

to implement a framework for beneﬁt-risk appraisal

of medicines in order to provide transparent and re-

sponsible beneﬁt-risk decision making model as de-

scribed in (Filip Mussen, 2009).

ACKNOWLEDGMENTS

We thank the National Association of Research and

Technology for funding this work, Ivanny Marchant

who helped to generate the VRP, and Nora Abchiche-

Habtoun for helping to produce high-quality images.

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