=Paper=
{{Paper
|id=Vol-1389/paper1
|storemode=property
|title=Argumentation for traceable reasoning in teleexpertise
|pdfUrl=https://ceur-ws.org/Vol-1389/paper1.pdf
|volume=Vol-1389
|dblpUrl=https://dblp.org/rec/conf/aime/DoumbouyaKKF15
}}
==Argumentation for traceable reasoning in teleexpertise==
https://ceur-ws.org/Vol-1389/paper1.pdf
Argumentation for Traceable Reasoning in
Teleexpertise
Mamadou Bilo DOUMBOUYA1,2 , Bernard KAMSU-FOGUEM1 , Hugues
KENFACK2 , and Clovis FOGUEM3
1
Université de Toulouse, Laboratoire de Génie de Production (LGP), EA 1905,
ENIT-INPT, 47 Avenue d’Azereix, BP 1629, 65016, Tarbes Cedex, France
{mdoumbou,bernard.kamsu-foguem}@enit.fr
2
Université de Toulouse, Faculté de droit, 2 rue du Doyen Gabriel Marty, 31042
Toulouse cedex 9, France
hugues.kenfack@ut-capitole.fr
3
Université de Bourgogne, Centre des Sciences du Goût et de l’Alimentation
(CSGA), UMR 6265 CNRS, UMR 1324 INRA, 9 E Boulevard Jeanne d’Arc, 21000
Dijon, France
cfoguem@yahoo.fr
Abstract. In this paper we propose a methodological framework based
on Artificial Intelligence tools such as Dung’s argumentation system in
order to provide a decision support tool to the medical professionals
performing an act of teleexpertise. The act of teleexpertise permits to
medical professionals with different skills and specialities to collaborate
remotely for taking suitable decisions for a patient diagnosis or treat-
ment. But in case of litigation, it is important to know where the errors
come from and who is the responsible of these errors. So by making the
decision making process traceable, it will be easy to identify who is the
responsible of the errors that lead to litigation. It is what we try to solve
in this paper by proposing a framework coupling semantic modelling and
argumentation system. A case study showing an act of teleexpertise to
treat an elderly with subdural hematoma is provided in order to illustrate
our proposal.
Keywords : Argumentation; Collaboration; Decision Making; Graph of attacks;
Teleexpertise.
1 Introduction
Telemedicine consists of performing medical acts remotely by the means of
telecommunication and information technologies. It allows the collaboration be-
tween different medical professionals and including sometimes the patient in this
collaboration in order to make suitable diagnosis and treatment of a disease. Its
main purposes [2] are: establishing a diagnosis, providing for a risky patient a
medical monitoring in the context of prevention or a therapeutic monitoring, re-
quiring expert advice, preparing a therapeutic decision, prescribing products, pre-
scribing or performing acts or services and monitoring a patient. Telemedicine
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is declined into four main acts: (i) teleconsulatation, (ii) medical telemonitoring,
(iii) teleexpertise, (iv) medical teleassistance. The acts are depicted in the Fig.
1.
Fig. 1. Main acts of telemedicine [12]
In this paper we are interested in the act of teleexpertise that is used by
medical professionals to seek remotely advices of one or more of others medical
professionals (with different skills or specialities) in order to take and make
decisions in a collaborative manner, which will lead to solve medical problems
related to a patient. In this act important decisions are taken, so the liability of
each stakeholder is engaged. Thus, in case of litigation it is very important to
know where the errors come from and who is or are the responsible. The most
important thing is to make the reasoning traceable in order to know who said
what.
When performing the process of teleexpertise, the advices given by the stake-
holders can be conflictual. In this the argumentative logic is used to provide the
potential acceptable arguments (advices). The notion of argumentative logic is
well explained in [12]. The acceptable arguments are returned to the requesting
physician who will make a final decision and store it.
In the following, the paper is divided into four sections namely: some related
works, the objective of this work, materials and methods section to show the
background of the argumentation logic and the analysis of results with case
study section and finally discussion and conclusion sections.
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2 Related Works
Many works have been achieved in order to help in finding the liability of each
stakeholders providing medical advices in case of litigations, for example Coa-
trieux et al., 2011 [10] and Bouslimi et al., 2012 [7]. Coatrieux et al. [10] used a
watermark technique for guaranteeing the traceability of the digital documents
containing medical data. It is the same idea as in [7], where the authors provided
a protocol that combines watermarking-encryption techniques and a third party
in order to easily bring evidences in case of litigations. Compared to our work,
the works achieved in [10] and [7] guarantee traceability by means of security
techniques while we guarantee traceability by means of storage and retrieval
techniques in a structured manner.
Concerning the argumentation applied to medicine, many works have been
also achieved. For example Hunter and Williams, 2012 [16] proposed an ag-
gregating evidence-based approach using argumentation for bringing evidences
about the positive and negative effects of medical treatments. Atkinson et al,
2006 [4] used argumentation to show how argumentation can be a value-added
asset for a collection of existing information agents. This process is applied to
a medical system for reasoning about medical treatments concerning a patient.
Jingyan et al., 2008 [17] used argumentation for collaborative practices in med-
ical emergency decision-making processes. Green, 2014 [15] described the role
that Artificial Intelligence models based on argumentation plays in medical do-
main particularly in personalised and participatory medicine. These works based
on argumentation are somewhat similar to ours, but the main difference is that,
in our work we used structured argumentation [5], which provides an internal
structure of arguments involved in the argumentation system.
3 Objectives
In this paper we want to provide a methodological framework taking both into
consideration semantic modelling and argumentation in the goal of aiding med-
ical professionals in their decision making process. This work aims to provide
innovative solutions coupling conceptual graphs (modelled by CoGui software
[1]) and Dung’s argumentation system [13] applied to telemedicine, which will
contribute to the telemedicine programs’ effectiveness [14]. One of the underlying
framework is called argumentative logic, which will permit to make the decision
making process traceable, in others words, to ensure the reasoning traceability.
Thus, by making the decision process traceable, one can identify clearly the ad-
vices provided by the medical professionals acting in a given act of teleexpertise.
To be clear, the main purpose of this work is to provide a tool to help create
favourable settings for effective interventions of medical professionals in act of
teleexpertise in order to know which of their different conflicting advices are
potentially acceptable. To do so, we use the Dung’s argumentation system in
order to model the conflicting arguments and build the acceptable arguments
under a given semantics (preferred, stable, . . .). These acceptable arguments will
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be returned to the requested physician after computation. This medical profes-
sional, according to some specific parameters (e.g. risk management, preferences,
. . .) and to the received acceptable advices will make a final decision i.e. choose
what advices are useful for the patient’s treatment. Furthermore, these chosen
advices will be stored for traceability and future expertise. Many works have
been achieved in the field of argumentation applied to medicine (e.g. Hunter et
Williams, 2012 [16]), but the novel contribution of this work is its positioning in
the highly collaborative segment of telemedicine that integrates additional con-
straints of remote collaborative decision-making in teleexpertise. Another con-
tribution of this work is that the modelling relies on conceptual graphs, which
provide an ontological knowledge with underlying logical semantic guaranteeing
logical arguments. Moreover, the reasoning is based on graph operations allowing
the visualisation of the reasoning steps using mainly the projection operations.
From the point of view of argumentation systems, our work deals with struc-
tured argumentation in which argument has internal structure [5] . The different
fields in the internal structure of the node are the same like those mentioned in
Table 1. Insofar as we combine semantic modelling and argumentation, the use of
CoGui software allowing the visualisation of the different steps of the reasoning
is important to display and store the satisfactory alternatives to queries. Thus,
the output of the argumentative logic is provided in a comprehensible form to
the requesting physician to enable him to reach an informed opinion. The storage
process guarantees the traceability of reasoning procedures.
4 Materials and Methods
4.1 Acceptability semantics
Above all, we define what is a decision framework (system) [6] also called argu-
mentation based framework AF [3].
Definition 1 An (argumentation-based) decision framework AF is a couple (A, D)
where:
– A is a set of arguments,
– D is a set of actions, supposed to be mutually exclusive,
– action: A → D is a function returning the action supported by an argument.
Definition 2 From an argumentation-based decision framework (A, D), an equiv-
alent argumentation framework AF = (A, Def ) is built where:
– A is the same set of arguments,
– Def ⊆ A × A is a defeat relation such that (α, β) ∈ Def if action(α) 6=
action(β).
Definition 3 Let AF = (A, Def ) be an argumentation framework, and let B ⊆
A
– B is conflict-free if there are no α, β ∈ B such that (α, β) ∈ Def .
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– B defends an argument α iff ∀β ∈ A, if (β, α) ∈ Def , then ∃γ ∈ B such
that (γ, β) ∈ Def
Definition 4 (Acceptability semantics) Let AF = (D, A, Def ) be a decision
system, and B be a conflict-free set of arguments.
– B is admissible extension iff it defends any element in B.
– B is a preferred extension iff B is a maximal (w.r.t set ⊆ ) admissible set.
– B is a stable extension iff it is a preferred extension that defeats any argu-
ment in A \ B.
Through these semantics of acceptability, the authors of [3] identify several ar-
guments’ status which are depicted below :
Definition 5 (Argument status) Let AF = (D, A, Def ) be a decision system,
and ε1 , . . . , εx its extensions under a given semantics. Let a ∈ A.
– a is skeptically accepted iff a ∈ εi , ∀εi with i = 1, . . . , x.
– a is credulously accepted iff ∃εi such that a ∈ εi .
– a is rejected iff @εi such that a ∈ εi .
The property that is directly connected to the above definition is specified as
follows:
Property 1 Let AF = (D, A, Def ) be a decision system, and ε1 , . . . , εx its
extensions under a given semantics. Let a ∈ A.
Tx
– a is skeptically accepted
Sx iff a ∈ i=1 εi
– a is rejected iff a ∈
/ i=1 εi
4.2 Analysis of results with case study
Case study. Ms D. 87 years old, living alone and having arterial hyperten-
sion and myocardial infarction as major medical history diagnosed six months
early and treated by Loxen (Nicardipine chlorhydrateR) 50 mg x 2/D, Corversyl
(PerindoprilR) 2,5 mg/D, Kardegic (AspirineR) 75 mg/D (midday) and Plavix
(ClopidogrelR) 75 mg/D (morning). She is admitted to the emergency depart-
ment of a local Hospital for a fall at home with an initial brief loss of conscious-
ness and caused by a head trauma. The emergency doctor who received the
patient performed a biological examination including a serum electrolytes, a C-
reactive Protein (CRP) and a blood count formula, which becomes normal. The
computed tomographic (CT) scan performed showed only a cortico-subcortical
atrophy without any sign of stroke nor hemorrhage. Thereafter, the patient was
allowed to back home with a simple diagnosis of brain contusion. Four days later,
Ms D. was admitted again to the emergency department for headaches. Another
emergency doctor performed again a second CT scanner, which showed a discrete
subdural hematoma. Given that Ms D. is an elderly and it is the second time
she was admitted, then she is a risky patient. The second emergency doctor who
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AI-AM/NetMed 2015 4th International Workshop on Artificial Intelligence and Assistive Medicine
received the patient decided to perform an act of teleexpertise. To do this, he
sought the advices of a geriatrician, a neurosurgeon and the attending physician
of the patient. After having taken the required expert advices, the neurosurgical
taken advice does not accept surgical indication. The advice provided by the
geriatrician is to perform immediately an invasive treatment, so he proposed to
make a surgery and the Attending physician of the patient decided to perform
invasive treatment (endoscopic surgery in order to assess the level of severity
the subdural hematoma) and then to perform a surgery if this latter is severe.
Finally the requesting physician (the second emergency doctor) decided to let
the patient back home again with the prescription to stop the Plavix and the
Kardegic is maintained.
Positioning of the stakeholders. According to the case study above the main
stakeholders acting are:
– The geriatrician: referring to the patient health state, he would like to
perform invasive treatment (endoscopic surgery in order to assess the level
of severity the subdural hematoma).
– The neurosurgeon: after receiving the CT scanner, he decided that there
is no need to perform surgery.
– The second emergency doctor: he decided to let the patient back home
with the prescription of stopping the Plavix and maintained the Kardegic.
– The attending physician: he knows very well his patient’s medical his-
tory. So he advised to perform endoscopic treatment followed by a surgical
intervention if the subdural hematoma is severe.
Modeling information available in structured arguments. To perform
a medical act, the medical professionals have the choice between invasive and
non-invasive treatment [9], this is resumed in the following:
– Maximisation of procedures (% P roc): it consists of performing invasive
treatments. It corresponds generally to surgical intervention.
– Minimisation of procedures (& P roc): it consists of performing non-
invasive treatments. In this option, the medical professionals perform medical
treatments such drug prescriptions, injection...
– It exists also a third option of treatment called medical technical treat-
ment. These treatments are at the frontier of surgery (for example endo-
scopic treatment). In this paper this option of treatment is modelled by
→ P roc.
The advices provided by the different medical professionals acting in this act
of teleexpertise are illustrated in the Table 1. In this table we can note that the
column “Concerns” is redundant because a graph of attacks is built only for a
group of stakeholders with the same “concern”. It is for this reason that “ensure
a good quality of life for this elderly patient” is redundant. It is the requesting
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physician who specifies the “concern” in his request of teleexpertise. So all the
stakeholders must give their advices on the basis on this “concern”.
In front of the clinical case described in the section 4.2, the system will be
used to ask remote advices. These advices are asked by the emergency doctor
who receives the patient when she was admitted again. When asking for the
teleexpertise, the requesting physician (the emergency doctor) designates the
required physicians by their specialities. On the basis of the patient medical
record, he chooses a Geriatrician, a Neurosurgeon and the Attending physician
of the patient while accompanying his request with his suggestion (advice) and
the concern. Each of the required physicians can express their advices in a struc-
tured manner according to the field (stakeholder, reason, concern, goal) of the
Table 1. Then a server gathers all the advices as shown in the Table 1, it trans-
lates them in conceptual graphs, builds the graph of attacks and then computes
the argumentative logic to know which arguments (advices) are potentially ac-
ceptable under a given semantics. Therefore, the output of the argumentative
logic is sent to the requesting physician who is empowered to make the final
decision that is stored in the server for potential subsequent verifications.
Table 1. Stakeholders argumentation
StakeholdersReasons Options Concerns Goals
1 Geriatrician α = He would like to % P roc Ensuring a Removing
perform immediately good quality the subdural
an invasive treat- of life for hematoma
ment, he proposed to this elderly even if it
make a surgery patient is not very
severe.
2 Neurosurgeon β = He decided that & P roc Ensuring a Preventing
there is no need to good quality the side ef-
perform surgery. of life for fects after
this elderly surgery.
patient
3 Emergency δ = He decided to & P roc Ensuring a Avoiding the
doctor let the patient back good quality blood coag-
home with the pre- of life for ulation by
scription of stopping this elderly stopping the
the Plavix and main- patient use of Plavix.
tained the Kardegic.
4 Attending γ = He would like → P roc ∧ % Ensuring a Assessing
physician to perform invasive P roc good quality the level of
treatment (endo- of life for severity of
scopic surgery in this elderly the subdural
order to assess the patient hematoma
level of severity the and perform-
subdural hematoma) ing a surgery
and perform a if needed.
surgery if this latter
is severe.
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Graph of attacks. The graph of attacks (Fig. 2) is a set of nodes linked
between them by oriented arcs. It is used in the argumentation system theory
[13] to represent the interaction existing between arguments.
α,% P roc
β,& P roc δ,& P roc
γ,→ P roc ∧ % P roc
Fig. 2. Graph of attacks
Decision making process. The different extensions below are determined
according the definitions above applied to the graph of attacks Fig. 2.
– Determination of conflict-free sets : the conflict-free sets are : {∅}, {α},
{β}, {δ}, {γ}, {β, δ}.
– Determination of admissible extensions : the admissible extensions
identified are : ε1 ={∅}, ε2 ={β}, ε3 ={δ}, ε3 ={γ}, ε4 ={β, δ}.
– Determination of preferred extensions : According to the definition
above the preferred extensions that we can have are: ε3 ={γ} and ε4 ={β, δ}
So by the definition above (argument’s status) and under the preferred se-
mantics, the arguments β, γ and δ are credulously accepted. These arguments
are then returned to the requesting physician for final decision. This final de-
cision will be taken under some additional parameters. So by considering these
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AI-AM/NetMed 2015 4th International Workshop on Artificial Intelligence and Assistive Medicine
parameters, the requesting physicians can decide to perform non-invasive treat-
ment (& P roc) or invasive and medical technical treatments (→ P roc ∧ % P roc
).
5 Discussions
The provided framework called argumentative logic based on Dung’s argumenta-
tion system guarantees the traceability on the reasoning in the decision making
process while permitting efficient collaboration between medical professionals.
Thus by the traceability, in case of litigation the responsibility of each medical
professional could be easily identified.
The use of Artificial Intelligence tool in the decision making process is taking
a big part in health domain generally and in telemedicine particularly. For exam-
ple the PANDORA system [8], used as learning tool in crisis environment (e.g.
health crisis) for decision makers with underlying Artificial Intelligence tools.
Comparing this one to our work, we can say that our proposal can also be used
as a learning tool since the accepted decisions are stored in a database [12] for
future acts of teleexpertise.
6 Conclusion
In this paper we proposed a methodological framework based on Artificial In-
telligence tools namely the Dung’s argumentation system [13] in order to aid
the medical professionals in their decision making process while ensuring the
reasoning traceability. This traceability will permit to identify the responsible of
medical errors in case of litigation [11].
In further work, we will implement our work to verify it feasibility. This
implementation will permit to the instantiation of the proposed argumentation
system in conceptual graphs in which we can represent rules and constraints.
Also given that CoGui software provides an API1 based on JAVA, it will be
possible to easily develop a kind of middleware to retrieve remotely medical
information to build the graphs of attacks in conceptual graph formalism.
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