The Delphi process applied to African Traditional Medicine

The Delphi process applied to African
Traditional Medicine

Ghislain Atemezing, Iván Garcı́a-Magariño, and Juan Pavón

Dep. Ingenerı́a del Software e Inteligencia Artificial
Facultad de Informática
Universidad Complutense Madrid, Spain
atemezing@yahoo.com, ivan gmg@fdi.ucm.es, jpavon@fdi.ucm.es

Summary. The African Traditional Medicine (ATM) has been applied over the
years in the ethnics of the African continent. The experts of this area have conserved
this knowledge by oral transmission. This knowledge varies from one ethnic to other,
thus it is distributed among these ethnics. In this context, this work proposes to
use ontologies and multi-agent systems to manage this distributed knowledge. The
presented approach uses the Delphi process, in which, there are several agents that
represent ATM healers and participate in several rounds of questionnaires in order
to reach a consensus for providing a heal to a patient.

Key words: African traditional medicine, multi-agent systems, software engineer-
ing, Delphi process

1 Introduction
African Traditional Medicine (ATM) [13] is the result of diverse experience,
mixing customs and knowledge about nature, which has been transmitted
by oral tradition along the history. Nowadays, the availability of computers
and networks in an increasing number of places around the African conti-
nent opens the possibility to consider the support of knowledge systems for
new practitioners, who can take benefit of ATM knowledge. African Tradi-
tional Medicine (ATM) uses many symbols and rites in its way to cure. It
distinguishes symptoms from illness. When a patient goes to a traditional-
practitioner, he suffers from something (an illness) that has a name in the set
of the diseases known to modern medicine. But the traditional-practitioner
seems to deal with a “social unrest” expressed by the tensions (revealed or
not) that could exist in the patient’s environment. Also, this medicine faces a
wide range of factors, causes of diseases and medicinal plants. However, this
knowledge did not have until now, a formal codification for a better under-
standing and management of concepts and the relations between them. The

2      Ghislain Atemezing, Iván Garcı́a-Magariño, and Juan Pavón

work [2] presents an ontology that best feeds with the ATM domain with the
particularity of being extendible to other type of traditional medicine.
    This work gathers the ATM knowledge in order to provide a prescription
for a patient, by means of a Multi-agent System (MAS) that uses Delphi
process. In this MAS, some agents represent ATM healers and each of these
agents have a piece of ATM knowledge. This MAS uses the Delphi [4] process
to reach consensus among agents and provide a prescription to the patient.
In the Delphi process, a monitor creates several rounds of questionnaires,
in which the ATM healers fill the questionnaires, and questionnaires include
some information of the previous rounds. In these manner, the system usually
evolves to reach a consensus. Before this work, the Delphi process obtained
good results for a MAS [9] that evaluates document relevance.
    This work uses the INGENIAS [17] methodology for the development and
its tool support, the INGENIAS Development Kit (IDK) [10], the notation of
the most relevant concepts of INGENIAS is presented in Figure 1.
    Next section describes the background concepts, which are: the ATM, and
the Delphi Method. Section 3 describes the application of the Delphi process
to the ATM, by means of a MAS. Finally, Section 4 mentions the conclusions
and future work.

                Fig. 1. The Most Relevant INGENIAS Notation.

2 Background Concepts

2.1 African Traditional Medicine

African Traditional Medicine (ATM) [13] is a complex system of cure in which
disease is considered as a social illness, which is necessary to eradicate from the
root. There is intervention of several actors from several domains, which turns
complex and diversified exchanges and treated knowledge. Several actors can
be identified in ATM, with specific roles and functions: the healer, who knows
the names of plants, animals and rocks; the fetishist, who predicts important
events and is consulted to find the cause of a disease; the soothsayer, who
predicts and is seeing as the intermediary with the divinity; and the magician,
who throws lots and makes use of black arts.
    In the process of treatment, a healer tries to reconcile the patient in all
its integrity, as well physical as psychic, by using symbols that are a part of
the universe and the life of every day of the patient during its interventions.

Delphi for African Traditional Medicine      3

In ATM, it is also necessary to distinguish symptoms from disease. When a
patient meets a traditional doctor, he suffers from the evil of which one can
attribute a name in human disease of the modern medicine. But for the tradi-
tional doctor, this patient is seen as a person who possesses a symptom, a sign
of a social illness. Social illness expresses tensions (hidden or revealed) that
could exist in the circle of acquaintances of the patient. Some anthropologists,
such as [18], introduce the concept of traditional model to express all that is
lived in the traditional vision. The body in this model consists of two entities:
a visible part and an invisible part. The global steps in the traditional model
are : interpretation of the cause of the bad physical appearance (sort of diag-
nosis); phase of divination to know how to treat the patient; and prescriptions
according to the cause of the disease and follow-up of the patient evolution in
the process of cure depending of the case [1].
    It is important to highlight that, in the traditional societies, there is no
distinction between the religious and the profane, but only between the visible
and the invisible; that is between the ancestors world and the alive one. One
believes much more easily in the thing that he does not see but that some
other people are able to see, than the one he/she sees. The family here is not
only composed of those who are alive but also of the death that live in an other
sense. The identity of each one is firstly within his family identity. One’s life
update his father live, his grandfather live, and so on. Thus someone’s disease
is the symptom of a collective illness. The traditional actor tries to find what
is going wrong in the family. It could maybe comes from a deceased, a living or
some relative closed to the family. For the traditional practitioner, the illness
of one family member is the clearly proof of his family conflict.

2.2 Delphi Method

The Delphi method [7] dates back to the fifties. It was created by the RAND
corporation in Santa Monica, California. It plans several rounds of questionar-
ies which are sent to the different involved experts. The results collected can
be included partially in a new round of questionaires, but respecting the
anonymity of the participants.
    This method was created initially for foresight studies, i.e., long-term de-
cisions that guide the policy of a country or a company. Besides forecasting,
there are many contexts where the Delphi Method can be applied, like reach-
ing a consensus in a community of experts [7]. The scenario considers several
experts discussing about a concrete topic. By using the Delphi method, indi-
vidual experts are forced to look at the reasons of other experts. This extra
information can force experts to reconsider their opinions and reach agree-
ments.
    An important part of the Delphi method consists in defining different
questionaires which are to be filled in by the different experts. These ques-
tionnaires intend to re-orient the initial problem. The re-orientation can be
elaborated according to the different answers supplied by experts. Therefore,

4      Ghislain Atemezing, Iván Garcı́a-Magariño, and Juan Pavón

each questionaire will include pieces of the answers already developed. By
the intervention of the questionaire elaborator, it is assumed that the process
converges in a single alternative. This mediator role is usually played by a hu-
man, though it could be replaced by a computer. This leads to the the Delphi
Conference, i.e., a computer based Delphi method [20].
    The Delphi Process in general is not rigid and its structure depends on the
situation. Looking for guidelines, this paper follows the steps and guidelines
stated in [6].
    The Delphi approach has been applied in several areas for different uses.
For instance, Roth [19] used the Delphi approach for acquiring knowledge
from multiple experts. Recently; Bryant [5] applied the Delphi method for
estimating the risk factors of the terrestrial chemical spill; Hayes [11] did a
Delphi study of the future of marketing of the higher education; Miro [16] ap-
plied the Delphi method to reach consensus among professionals with interest
in chronic pain among children and adolescents.
    The automatization of Delphi is considered first as a set of computers
and software assisting human experts in the process. In this line, literature
mentions DEMOS[15], which is an on-line discussion system based on Del-
phi, and Turoff [20], who presents a Delphi method with computer assistance.
Holsapple [12] provided a framework based on Delphi methodology. Within
this framework the processors (human and/or computer-based) manipulates
knowledge resources. This framework is descriptive, but considers and en-
courages the possibility of computer-based processors integrated in a Delphi
organization. However, the Delphi method was not completely computerized
until our previous work about document relevance [].

3 Delphi process for ATM
The Delphi method can make a MAS reach a consensus [8]. The current
work applies this method in the African traditional medicine. Within this
section, Subsection 3.1 describes the general overview, Subsections 3.2 and
3.3 respectively indicate how the questionnaires are replied and managed.
Subsection 3.4 describes the ontology of the presented system.

3.1 General Overview

In this application of Delphi process, the experts are the ATM healers, and
they are modeled with a collection of cases. In this approach, the main use
cases (see Figure 2) are the following:
• beCuredUC : The MAS indicate the way a patient can be cured.
• delphiUC : In this use case, the agents, representing the healers of ATM,
  cooperate to obtain the best cure for a patient.
    The MAS uses the following agents for this application of Delphi process:

Delphi for African Traditional Medicine       5

Fig. 2. Main use cases considered in the development of the Delphi process for ATM

•   Patient agent: This agent is the interface for the patient. The patient
    indicate its illness to this agent. This agent asks for the other agent to
    obtain a cure for its patient.
•   ATMHealer agent: Each of these agents has a particular piece of knowledge
    of the ATM. This knowledge is managed with ontologies. It can provide a
    prescription for each illness. This agent can also be flexible and consider
    other opinions and slightly alter its original prescription.
•   Monitor agent: This agent can organize a Delphi process, in which it makes
    a group of different ATMHealer agents to reach a consensus.
    The workflow (see Figure 3) of this MAS of ATM follows the Delphi pro-
cess. Firstly, in task FillSymptoms, the patient fills a form with its symptoms
and the agent Patient inquires a prescription to the Monitor agent. Then,
the form is received by the Monitor agent and a customized questionaire is
elaborated with task InitQuestT. The questionaire is answered by ATMHealer
agents by means of a task AnswerQuestT. The answer is processed by the Mon-
itor with a task ProcessAnswerT. As a result of this task, another round can
be derived or not. If a new round occurs, the task CreateOtherQuestT should
be executed. This would force another elaboration of questionnaires and a
new answer delivered by experts. If no more rounds occur, then the Monitor
delivers the result to the Patient agent, which processes the prescription with
task ResultObtainedT and presents it to the patient.

3.2 Replying the Questionnaires

The ATM healers reply the questionnaires. In the first round, each ATM
healer receives input from the symptoms and indicates a prescription. Each
ATM healer agent has a piece of knowledge, which is represented with a
collection of African-medicine cases. These ATM healer agents use the Case
Base Reasoning (CBR) [21] to reply questionnaire. A case here is constituted
with: the symptoms, the illness, the healer experience of the case and the
prescription.

6      Ghislain Atemezing, Iván Garcı́a-Magariño, and Juan Pavón

Fig. 3. Overview of the workflow used to implement the Delphi process for ATM

   The symptoms consist of the following attributes: Family (polygamic,
monogamic, large); weight (constant or no); fever( yes or no); vomiting (fre-
quently, si or no); human part (one of the sixteen division we have adopted
from [14]); pain type (serious or no), patient’s category (child, adolescent,
adult and old); and sex.
   The names of the illness in ATM changes from one region to another region.
Thus, the ontology is necessary to narrow the diversity of terminology. The
ATM healer experience indicates the reliability of the case. Both the illness
and experience are taken into account for considering the answers from other
ATM healers.
   The prescription consists of the following attributes:
• Operatory mode: that describes how the remedy is obtain or the technics
  used to obtain the remedy.
• Posology: which describes how to take the remedy.
• Medicine Group: which is one of the fourteen medicine groups which,
  among others, can be respectively: drinked, breathed, purge, ointed, gar-
  gled and licked.
• Requirements: which are some previous conditions to make use of plants
  or organic minerals in a composition or/and posology.
• Compositions: which are plants names or minerals that enter in the com-
  position of the potion or remedy.
   For replying the questionnaires, the symptoms are used to retrieve the
most similar case. The similarity function uses the cosine of the similarity for
each attribute. The similarity of the most symptoms attributes is calculated

Delphi for African Traditional Medicine       7

by means of the ATM ontology or some table of values that indicates the
similarity between two values. These ontology and similarity values are the
results from a collaborative work with ten ATM healers, who currently lives
in Cameroon (a Central African country). The questionnaires are filled with
both the illness and the prescription of the retrieved case.
    Since the agent should save and learn from cases, this work uses the JCol-
ibri [3] CBR tool. Each agent has casebase in a plain text with a mapping
connector to the CBR, where the columns are the relevant attributes of the
forms questionnaires filled by the patient agent. The use of the CBR Tool
allows one to make use of the similarities functions already existed and im-
plemented within the tool.

3.3 Considering the Replies for Reaching Consensus

The Monitor agent creates questionnaires considering the Replies of the pre-
vious Rounds. Then, ATM healer agents consider the replies of other ATM
healer agents when these questionnaires are replied. This consideration is
based on the illness and the expertise retrieved for the collections of cases.
    Each Agent has his experience of treatment, that is all the cases of his
different practices. We use an attribute to balance this experience for each
disease case (EC ), a real that is ranged in the [0,1] interval and is communi-
cated to others agents to justify its point of view.
    In the rounds of this process, different situations could be raised, which
the Monitor agent manages with the following rules:
Rule 1 if there are more than one agent with the same score, the solution of
these agents is adopted for the case

Rule 2 if there is one agent with a score greater than 0.9 and all the others
with a score less than 0.5, the solution is the one proposed by that agent.

Rule 3 If there are many scores (more than 80 %) less than 0.5, each agent
calculates and returns his Reputation Score RS (see Equation 1) If one has
RS > 0.9, this rule follows Rule 1 and Rule 2.
                                    α ∗ RM + β ∗ EC
                             RS =                                               (1)
                                         α+β
where
RM is the Reputation of the Master 1 of the healer.
EC is the Experience of the healer in the Case
α, β are constant parameter values
α + β greater or equal to one

1
    The master is the person from whom a healer learns. The reputation of the master
    is obtained by asking the patients of the master.

8      Ghislain Atemezing, Iván Garcı́a-Magariño, and Juan Pavón

Rule 4 Otherwise the Monitor starts a new round of questionnaires. The
ATM healer agents are expected to smoothly change their replies. For this
reason, the agents that strongly change their reply are considered as non-
reliable, and they are omitted in the next round.
Rule 5 If there is no one who knows the case, the case must be treated by
a human ATM healer. Each ATM healer agent saves the case in his KB and
learn from it.

3.4 The Ontology

                    Fig. 4. An excerpt of ontology for ATM

    The ontology now covers up to 152 diseases, classified in 28 different cat-
egories (by body parts); 26 remedies or potions, 42 vegetable products that
enter in the elaboration of these remedies [14]; 11 groups de potions and 3
conditions lying in the prescription of some medicines.
    An example of the knowledge in this Knowledge Base is one of the following
in the Duala language (spoken in many region of Cameroon coast) , “mulopo
ma mbomo” is considered as Infantile Acute Headache and a well-known

Delphi for African Traditional Medicine       9

treatment for it is: “Crush mundo ma ngue and mix it with cool water. Filter
and purge the child: two or three small pears. First purge the child with simple
water”.
    The agents used mainly the following classes of the Ontology: Diagnos-
tic, Disease, MedicineGroup, DiseaseGroup Medicine and Requirements. The
extract of the used classes can be seen in the Figure 4.

4 Conclusions and Future Work
In conclusion, this work proposes a MAS that combines the Delphi pro-
cess with the Case Base Reasoning (CBR) for African Traditional Medicine
(ATM). The Delphi method regards on the domain, and it was necessary that
several experts of ATM provide the necessary information to the engineers.
    For future work, the presented MAS can be tested with patients, in order
to validate the system with empirical results. In addition, for the future, this
work pursues another goal, which is to prove that the Delphi method is a
reliable mechanism to reach consensus in MAS. The first step was to apply
the Delphi method in Document relevance. In this second work, the Delphi
method is applied in African traditional medicine.

Acknowledgments.

This work has been supported by the following projects: Methods and tools
for agent-based modeling supported by Spanish Council for Science and Tech-
nology with grant TIN2005-08501-C03-01; Methods and tools for agent-based
modeling supported by Spanish Council for Science and Technology with grant
TIN2005-08501-C03-03 and Grant for Research Group 910494 by the Region
of Madrid (Comunidad de Madrid) and the Universidad Complutense Madrid;
and Methods and tools for agent-based modeling project with grant TIN2005-
08501-C03-01 and Grant for Research Group 92354, supported by the Univer-
sidad Complutense Madrid in 2008.

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