Argumentation schemes for clinical decision support

1.1. Motivation

The process of deciding what is the best treatment for a patient involves integrating data from multiple data sources with one or more clinical guidelines. This is already a difficult problem for hard-pressed clinicians to achieve in the short time that they are allotted per patient, and is only getting worse as resources are further squeezed. There are ever increasing numbers of people living with multiple chronic conditions, with treatment governed by different, sometimes conflicting, guidelines. Moreover, there is a proliferation of additional relevant data (e.g., from well-being sensors). It is thus ever more important to efficiently provide personalised advice, such as advice on medication or lifestyle changes, in ways that take proper account of all the information. Furthermore, it is also necessary to ensure consistency in the interpretation of patient data and guidelines [8]. The use of decision support tools provides one way to integrate these disparate sources of information in a consistent way.

The suggestion that decision support systems can help in a clinical setting is not new [16]. There is evidence that the use of Decision Support improves GP performance [30], and yet adoption is limited [50]. A survey [22], that aims to outline the reasons for the lack of adoption of clinical decision support systems (CDS) in clinical practice, lists both inadequate patient specificity, particularly in multi-morbidity situations, and the difficulties in sharing knowledge from different systems as some of the reasons. In [39], the authors note the lack of integration into clinical work as a barrier to adoption of CDS in general practice. In the UK, the National Health Service (NHS) standard length for a consultation is 10 minutes. Feedback [40] reported through Focus Groups that we held with patients, carers and clinicians to discuss barriers to the adoption of CDS, included the need to condense all the different sources of data into meaningful summaries and personalised recommendations. The concern raised was that there is not enough time within a 10 minute conversation to make sense of the data, and its implications on treatment, and to discuss options effectively. We interpret this as saying that CDS need to provide more support for integrating data from different sources, for understanding treatment decisions, and for presenting alternative treatment options and the trade-offs between them. In particular, CDS need the ability to provide justifications for any recommendations made [22, 39].

Argumentation for medical decision making has a long and established history, for example [15, 18]. There are several reasons why argumentation is helpful in this context. First, it provides an abstract formalisation of logic-based reasoning over conflicting information. Second, the exchange of arguments enables one to rationally deliberate and critique possible actions (or treatments) and to reason over different pieces of information in a modular manner. The advantages of modularising such reasoning in the form of interacting arguments, is that one can readily integrate information from independent sources [21, 52]. Third, argumentation-based formalisations of logical reasoning appeal to principles familiar in everyday reasoning and debate, and so render the reasoning process more readily understandable. This in turn means that the integration of human input into the reasoning process is more readily facilitated and the resulting audit trails of exchanged arguments provide evidence of due diligence with regards to the decisions made. Finally, argumentation can also accommodate reasoning with and about possibly conflicting preferences (e.g., over arguments supporting alternative treatments) that may arise from different knowledge sources and the perspectives of different clinicians.

The advantages of argumentation-based approaches have never been more relevant given multiple co-morbidities and hence possibly conflicting guidelines, requiring explanations of decisions that can be readily communicated to physicians, GPs, and patients, and moreover requiring input from physicians/GPs as well as patients in order to account for their preferences/values. Indeed, such requirements are particularly pressing given the increasing use of black box machine learning approaches whose decisions need to be rendered explainable and parameterised by human inputs. Furthermore the models upon which the decisions are made need to be trustworthy, ensure transparency and provide evidence of due diligence [46]. There is also scope for argumentation based approaches in reasoning with conflicts between different guidelines and incorporating preferences/values as part of the design [9]; however these are not in scope for this paper.

1.2. Approach

The approach we present in this paper builds upon Walton’s work on argumentation schemes (AS) [57]. Argumentation schemes can be used as inference licences, as dialogical devices, or, as in our case, they can be employed as a way of expressing reasoning templates [42]. In other words, they are a mechanism that supports the generation of arguments that align with common patterns of reasoning. For example, the scheme “Argument from Expert Opinion” [56] captures the stereotypical form of argument whereby if someone is an expert in some domain, and they make an assertion in that domain, then one might reasonably assume that the assertion is true. Defining argumentation schemes is thus a form of knowledge elicitation, specific to the use of argumentation. Moreover, since computational argumentation is concerned with the evaluation of arguments and counter-arguments for candidate options, argument schemes are associated with scheme specific critical questions (CQs). While argumentation schemes effectively provide templates for capturing the rationale for some claim, the scheme’s critical questions help identify possible counter-arguments and elicit further rationales (i.e. arguments) supporting the scheme’s premises. For example, critical questions associated with the ‘Argument from Expert Opinion’ scheme include querying whether the person in question is indeed an expert, and whether other experts agree on the assertion being made [56]. Posing the first question may involve constructing a (counter) argument claiming that the person is not an expert, or placing the burden of proof on the agent constructing the argument from expert opinion to provide some further argument justifying the claim that the person in question is indeed an expert. A practical clinical application of argumentation schemes is studied in [53].

Argumentation schemes provide generic templates for constructing arguments, via the provision of concrete values for the variables in these templates; a process referred to as instantiation of the scheme. As outlined above, the CQs associated with each scheme can be used to identify possible counter-arguments or elicit further rationales for the premises of an argument. In this paper we focus on the former: the instantiation of a CQ generates a counter-argument to the source argument obtained by instantiating the scheme. A counter-argument can challenge the source argument by challenging a premise or the conclusion of the source argument.

The advantage of using argumentation schemes and critical questions is that they provide a bridging formalism between data and knowledge in logic-based or relational languages and human understandable representations of dialectical reasoning. They also motivate exploration of the full space of dialectical reasoning w.r.t. a topic, so fulfilling the due diligence requirement, which is especially important in safety critical decision support such as clinical decision support. The use of schemes and CQs has been explored for clinical decision making [4, 21, 53], but practical deployment suggests that existing schemes are too generic for use by clinicians [53]. A response, explored in [53], is to propose domain specific specialisations of generic schemes and CQs, so that they can effectively be implemented to leverage the above mentioned advantages for medical decision making.

In this paper, we are similarly motivated to develop specialised schemes and CQs for deliberation over medical treatment proposals, and moreover to operationalise their use in the ways described above. To be clear, this paper presents the first step in a more ambitious long term program of fully mapping out and operationalising reasoning about medical treatments in the form of specialised schemes and CQs.

1.3. Contribution

There has been previous work on the application of argumentation schemes to various clinical and decision support situations. For example, specialisation of schemes for decision support within the clinical domain was outlined in [53] in the context of organ donation. However the specialisation of argumentation schemes to the process of supporting decisions on treatments using clinical guidelines, is novel. The work outlined in this paper builds on previous work that introduced the specialised scheme ASPT [28]. Our proposed specialisation of the schemes enables us to differentiate between different clinical rationales that would preclude an action, for example the difference between a contraindication due to demographics (e.g. ethnicity or age) as opposed to one due to a past bad reaction.

Such argumentation schemes can be used by a decision support system or learning health system to pre-process and narrow down the treatment options for a given patient. The advantage of the use of computational argumentation for this is that the rationale for any recommendation made can be provided both to support the recommendation to consider or not to consider a specific treatment. The schemes identified here are a comprehensive set and can be directly implemented to create a clinical decision support system that integrate the relevant guidelines and patient observations. Such a system has the potential to improve patient consultations by allowing more time to be spent in discussing treatment options, as opposed to finding them [41].

The paper also articulates an approach to operationalise the specialised schemes, and illustrates the application of the schemes using case studies related to hypertension treatment as part of secondary stroke prevention [25, 26]. We also share details of our implementation which includes the formalisation of the clinical guideline for hypertension, and demonstrates the applicability of the argumentation schemes that we propose.

1.4. Methodology

Finally, a few words about how we arrived at the argument schemes, and the implementation, that is presented here. The development of the argumentation schemes was carried out through knowledge elicitation from a general practitioner (GP).

This was time-consuming, as is often the case with knowledge elicitation processes. However, there are two aspects that suggest that building systems that make use of argumentation schemes is not as gravely affected by the “knowledge acquisition bottleneck” as the construction of other systems is. First, because argumentation schemes are, as the name suggests, schemas for generating arguments, they formalize knowledge at a more abstract level than the rules or Bayesian network relations that are typically yielded by knowledge elicitation. Hence, schemes and their associated critical questions are modular and reusable representations of reasoning patterns, playing a similar role in the structuring of inference mechanisms as ontologies do in the structuring of symbolic knowledge bases. Thus, we anticipate that future work on argumentation scheme-based clinical decision making will be able to reuse much of what we present here. Second, the domain knowledge that is used along with the argumentation schemes, is derived from clinical guidelines. These guidelines are already highly structured representations, distilled from clinical knowledge and medical evidence, by experts. Such information can then be represented as a formal language that would support automated reasoning, as we demonstrate in this paper.

A final issue is the validation and evaluation of the schemes. Validation was performed in the context of a set of case studies relating to the management of hypertension.1

2.1. Clinical background

When a clinician deliberates about what treatment to prescribe in order to attain a specific goal for a patient, they rely on clinical guidelines. For example, in the UK, NICE (National Institute for Clinical Excellence) issues guidelines, distilled from medical evidence collected in the literature, that are intended to guide clinicians’ decisions, and provide standardised approaches to handling conditions. For example, there is a NICE guideline for reducing a patient’s blood pressure (BP) when they are suffering from hypertension.2

The process of narrowing down and deciding on a specific treatment involves an initial generic inference that in effect provides a starting list of treatment options, given the patient’s goal. This list then needs to be challenged by ascertaining that there are no contraindications for a specific treatment currently in the options list. Another consideration is whether there is treatment-specific experience from the patient’s clinical record that may impact its suitability. The latter could be a previous allergic reaction, a previous side effect or previous ineffectiveness. Obviously the last consideration is relevant only if the patient has direct experience of the treatment under consideration.

The process of determining the possible treatment options involves querying the clinical guidelines and asking questions to determine whether the proposed treatment is suitable for the given clinical objective, and for the specific patient. The questions include:

Which treatments in the guidelines achieve the clinical goal?

The idea of argumentation (‘argument’) scheme (AS) originally emerged from the domain of informal logic, in particular, from the work of Doug Walton [57, 58]. An AS is a semi-formal reasoning template that matches common reasoning patterns in real life. More formally [26], an AS = ⟨ S , c , V ⟩ is a set of support premises (S) which support the conclusion c, where variables ( V ) in the AS appear in the premises ( S . V ) or the conclusion ( c . V ). These variables can then be instantiated by ground terms to yield a specific argument. Consider the “Argument Scheme for Practical Reasoning – the “sufficient condition scheme”, from [58]:

We might instantiate G with the goal “get from London to Edinburgh” and a with the individual “Amy”. In our example the support premises are: “Getting to Edinburgh” and “Taking a train is sufficient for Amy to get to Edinburgh”. The conclusion of the scheme is that “Amy should take the train”.

One of the key features of argumentation schemes is the list of associated critical questions (CQs). The premises, claim (or conclusion) supported by the scheme are presumptive and a premise or claim is withdrawn unless the CQs posed have been answered successfully. In general, posing CQs can also be used as pointers to counter-arguments that themselves instantiate argument schemes. In this paper, we adopt this latter use, and thus represent the CQs as argument schemes that can be used to construct counter-arguments. Of course, these argument schemes have their own critical questions, and in this way, the instantiation of schemes thus yields graphs of attacking arguments. A similar such use of critical questions was formalised in the modelling of legal reasoning with preferences in [44]. Whenever the CQs are posed, they can be answered in two different ways. First, a knowledge base can be used to generate arguments automatically. Second, a human can construct arguments. We will assume both options in this paper. The instantiation of the appropriate argumentation scheme and its associated CQs is a way of generating arguments that can then be evaluated when organised into an argumentation framework, which we introduce in Section 2.3.

The argument schemes we propose as good candidates to use as a basis for the clinical specialization in support of reasoning about clinical treatment options are: the Argument Scheme for Practical Reasoning (ASPR) in Table 1 [3] (this is an extended version of the scheme introduced above), the Argument Scheme from Negative Consequences (ASNC) in Table 2 [57], the Abductive Argument Scheme for Argument from Effect to Cause (ASEtoC) in Table 3 [57], the Argument Scheme from Analogy (ASA) in Table 4 [57] and the Argument Scheme from Danger Appeal (ASDA) in Table 5 [57]. ASPR is a general scheme that can assist in reasoning about what actions to take in given circumstances [3] [44], and it is more comprehensive than the version introduced in our simple travel example. In the clinical setting, the actions being considered are possible treatments. ASNC can help when reasoning about side effects, and in turn ASEtoC can assist in reasoning about whether a side effect can indeed be attributed to the treatment being considered. ASDA can be used to generate arguments related to contraindications, when a particular treatment is known to be harmful in patients with specific characteristics. When specialising the schemes we make a distinction between a side effect and a contraindication. The former is experienced after trying a treatment, and may not rule out the use of a treatment. The latter is known once the patient’s details are known and will rule out the use of the treatment considered. In the case of a contraindication, an alternative will need to be considered. ASA can support reasoning about whether a treatment has achieved the desired goal in past similar situations on the same patient.

ASPR (as outlined in Table 1) has 16 critical questions associated with it. The purpose of these CQs is to identify the ways in which the premises may be challenged, and arguments grounding negative answers to the CQs can be seen as attacks on the original argument. For example if ASPR is used to reason about action when a person has a temperature (fever), then CQ1 queries the presence of the fever. A negative response would invalidate the argument to take a treatment to lower temperature, as the circumstances are not true. In previous work [28], we introduced a specialisation of this scheme, Argument Scheme for Possible Treatment (ASPT), and alongside it we gave applicable critical questions in the context of clinical treatments. We will not be revisiting the creation of this scheme as a specialisation of ASPR and we will leverage the reduced list (with respect to those for ASPR) of applicable critical questions as defined in [28].

Computational argumentation is an established method for reasoning with incomplete, and at times conflicting, information or knowledge [45]. An argument is structured so that it has a conclusion or a claim, and the support for the claim. One way to define such arguments is through the instantiation of argument schemes. When argumentation is employed in the context of decision support, its structure supports a more human like reasoning process where arguments’ conclusions, their support and their relations can be modelled. Argumentation has been extensively explored within the multi-agent systems community [3, 29, 51], and in Section 5 we discuss previous work on using argumentation in clinical decision support systems (DSS).

While there are several different formal models of argumentation, for example [2, 17, 37], all models include the notion of an argument, which we denote with Arg = ⟨ S , c ⟩ , which consists of a set of premises, S, defined in some language, L , which support the conclusion, c. An Argumentation Framework (AF) [12] represents a set of Arguments A , and the relationships between the members of the set. Formally an AF is a pair ⟨ A , R ⟩ , where A is a set of arguments and R ⊆ A 2 is a binary attack (counter-argument) relation between arguments. When using argumentation schemes, we suggest that attack relations come from the instantiation of the CQs associated with a scheme. We explain in detail how this can be done in Section 3.3, but consider the argumentation framework depicted in Fig. 1, where the boxes represent the arguments and the arrows represent the attack relation between them. For example, Arg 1 and Arg 2 are two arguments; the arrow from Arg 2 to Arg 1 indicates that Arg 2 attacks Arg 1 , i.e., ( Arg 2 , Arg 1 ) ∈ R . If Arg 1 is an argument instantiated from a scheme AS 1 , and AS 1 is associated with a critical question that is represented as another scheme AS 2 , then Arg 2 , an argument instantiating AS 2 , will attack Arg 1 . Then, if there is a CQ associated with AS 2 that is represented as another scheme AS 3 , the instantiation of AS 3 will yield another argument Arg 3 which will attack Arg 2 . In this simple example, the argumentation framework can be formally represented as ⟨ { Arg 1 , Arg 2 , Arg 3 } , { ( Arg 2 , Arg 1 ) , ( Arg 3 , Arg 2 ) } ⟩ .

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Fig. 1.

An example of an argument framework where Arg 1 is an argument instantiated from a scheme AS 1 . The critical question to AS 1 is instantiated using another scheme AS 2 and as such Arg 2 , an argument instantiated from AS 2 , will attack Arg 1 . The critical question to AS 2 is instantiated using another scheme AS 3 , and argument generated from AS 2 will attack Arg 2 .

Given an AF ( ⟨ A , R ⟩ ), one can identify subsets of arguments that are coherently justified [12] (these coherent sets then represent views that we might consider as rational conclusions from the AF). Let S ⊆ A be a set of arguments. We say S is conflict-free (cf) iff S 2 ∩ R = ∅ ; i.e., no two arguments in S attack each other. We say an argument Arg ∈ A is acceptable w.r.t. S iff all attackers of Arg are in turn attacked by some argument in S. For any S let d ( S ) ⊆ A denote the set of arguments acceptable w.r.t. S. We say S is self-defending iff S ⊆ d ( S ) . We say S is admissible iff it is conflict free and self defending. Intuitively, admissible sets of arguments represent justified sets of arguments that are collectively consistent and can respond to all their counter-arguments. Thus admissibility is typically taken as the minimal requirement for a set of arguments to be considered reasonable. There are a range of more stringent requirements, identifying more restricted sets of arguments that can be accepted. Such sets of arguments are called extensions, and the approaches for determining which arguments are accepted are called semantics such as grounded and preferred. The grounded extension, is defined to be the ⊆-smallest admissible set satisfying S = d ( S ) ; S is the preferred extension, iff S is a ⊆-maximal complete extension. Given the framework in Fig. 1:

In Section 4, we provide case studies where we consider the preferred semantics that give possible sets of acceptable arguments; then a clinician decides on a treatment option accordingly.

As already discussed, argumentation schemes are instantiated to construct arguments. Attack relations between arguments are then added, based on several different kinds of knowledge. One type, as discussed above, takes the form of critical questions for argumentation schemes. Another type of knowledge that leads to attack relations is domain knowledge. For example, in the case of clinical decision support, this domain knowledge could be about two treatments that are alternatives, so that an argument for one is an argument against another. A final kind of knowledge leading to attack relations is domain independent. An example of this is that an argument for something being true can be an argument against it being false. The sets of arguments and attacks are then used to construct an argumentation framework, and that can be evaluated as articulated in Section 2.3. Acceptable arguments and extensions can be computed in order to identify the possible treatment options, and reasons for any treatment not to be considered can be provided. There are many approaches to reasoning and supporting recommendations given an argumentation framework and the choice of approach or method is independent of the fact these arguments were defined using a specific set of schemes.

The use of argument schemes to generate argumentation frameworks can be carried out in several ways. ASPIC + is a framework for structured computational argumentation that supports the generation of arguments and attacks by using a knowledge base including axioms and rules [37]. For example, argumentation schemes can be represented as defeasible rules. Another approach, which allows for a broad range of domain-dependent attacks between arguments, is that described in [26], in which attack schemes can be instantiated so as to identify attack relations. We have also explored the use of metalevel argumentation frameworks (MAFs) [36] to support medical decisions [25, 27]. MAFs allow the rationales for attacks can be provided as meta-arguments. Therefore, challenging the rationale for any given attack becomes possible. Moreover, meta-arguments providing rationales for preferences (i.e., preferences between other ‘object-level’ arguments) can be constructed, so enabling reasoning about possibly conflicting preferences (e.g., when clinicians’ preferences for treatment options may conflict). These approaches are not mutually exclusive since, for example, attack schemes can also be represented in MAFs.

The specialised schemes we propose will form part of the decision making process that occurs after a clinician has made a diagnosis and is in the process of deciding treatments. The diagnosis will be associated with a clinical goal; for example if there is a diagnosis of hypertension (high blood pressure) then the associated clinical goal would be to lower the patient’s blood pressure. The reasoning process that needs to be captured by the specialised schemes should include proposing treatments from the applicable clinical guidelines, ensuring that there are no contraindications to the treatment for the patient, ascertaining whether this treatment has been taken by the patient in the past and has not been effective or has caused side effects that made the treatment intolerable. Furthermore as there are typically multiple treatment options, this reasoning process also needs to ensure that all the alternative options, relevant at the stage of treatment are considered.

To capture this reasoning process, we introduce five new argument schemes, which are specializations of the schemes introduced in Section 2.3. Table 6 contains the relation between the original schemes and the clinically specialised ones that we propose. These schemes are: argument scheme for proposed treatment (ASPT) [28], argument scheme from negative side effect (ASnegSE), argument scheme from side effect to cause (AS_Eff_Cause), argument scheme for contraindication (ASContraInd) and argument scheme from Patient Medical History (ASPatMedHist).

The relationship between the specialised schemes are illustrated in Fig. 2 where the arrows point from the attacking argument (instantiating the scheme representing the critical question) to the attacked argument (instantiating the argument scheme whose CQ is posed in the form of the attacking argument). Schemes are denoted by boxes, critical questions by ovals. Thus the scheme ASPT at the top of the figure has four critical questions represented by the ovals connected to ASPT by arrows. The four critical questions (including the alternative action) all represent possible ways to generate additional arguments relevant to the applicability of ASPT. We can also see which argument scheme is applicable for each of these critical questions, for example the Argument Scheme from Patient Medical History’s role is as the first critical question of ASPT. The notation ASXX.CQy represents the yth critical question of the Argumentation Scheme ASXX. Therefore ASPatMedHist is ASPT.CQ1.

As is clear from its position in Fig. 2, the key scheme here is ASPT. This is the Argument Scheme for Proposed Treatment (ASPT) [28], which we derived from the Argument Scheme for Practical Reasoning [3] to reason specifically about clinical treatments and actions. ASPT instantiates an argument in support of each possible treatment, given the known facts F about the patient and the treatment goal G to be realized, e.g. lowering the patient’s BP. The arguments instantiated by this scheme are all subject to CQs. It is imperative that before proposing the use of a specific treatment all possible objections to its use, or alternative approaches should be explored. In this case the critical questions are used to instantiate counterarguments to the arguments instantiated by ASPT.

ASPT as proposed in [28] with the addition of new critical question (CQ4) is in Table 7. This additional critical question is one derived from ASPR’s CQ6 (“Are there alternative ways of realising the same goal?”). The patient facts F include (amongst possibly other information) their age, BP (including stage), ethnicity and previous treatments and relevant clinical history (such as allergies).

By taking each of the critical questions of ASPT in turn we can assess what existing argument scheme template best suits each of the critical questions. Starting with ASPT.CQ1 which asks “Has treatment T been unsuccessfully used on this patient in the past?”. In other words ASPT.CQ1 wants confirmation that if this treatment was used in the past, there is no evidence that it did not help and was not effective. If a treatment was not effective on a specific patient in the past then this provides a counter argument to the repeat use of the same treatment. The Argument Scheme from Analogy provides a starting template for ASPT.CQ1 [57] and the specialised scheme we propose is the Argument Scheme from Patient Medical History (ASPatMedHist). This scheme is outlined with its own critical questions in Table 8. The critical questions of ASPatMedHist can also be specialised; however we leave this to future work.

ASPT.CQ2 can be addressed by an argument instantiating a specialisation from the Argument Scheme from Negative Consequences as it queries the past occurrence of undesirable side effects that have caused the patient to not tolerate the treatment. The difference between ASPT.CQ1 and ASPT.CQ2 is that the latter does not consider whether the treatment was effective, only if it caused some undesirable side effect. We assume that treatments can be effective, yet not be tolerable due to the side effects. The Argument Scheme from Negative Consequences in Table 2 needs to be modified to support the treatment recommendation, its specialised representation Argument Scheme from Negative Side Effect (ASnegSE) is in Table 9. This scheme has two critical questions: ASnegSE.CQ1 which is concerned with the possibility of ameliorating the side effects observed; ASnegSE.CQ2 probes whether the side effect could have an alternative explanation (or cause).

The instantiation of the first CQ of the AS from Negative Side Effect (ASnegSE.CQ1) generates a new clinical objective or goal, which can then lead to the re-use of ASPT with this modified goal.3

ASPT.CQ3 is concerned with considering known contraindications for a treatment given specific circumstances or patient facts. These sets of contraindications are extracted as rules from the clinical guidelines. For example, if a specific type of treatment is not to be used above a specific age (for example Ibuprofen can cause Gastrointestinal (GI) bleeds in patients above a certain age), then this will generate a counter argument to the use of that treatment for the patient. The Argument Scheme from Danger Appeal is used as a basis for the specialised Argument Scheme for Contraindication. The Argument Scheme for Contraindication scheme is outlined in Table 11. The Argument Scheme from Danger Appeal does not have Critical Questions therefore the Argument Scheme for Contraindications does not have any either. In cases where a contraindication is identified ASPT should be instantiated on alternative treatments for the same goal.

Finally ASPT.CQ4 expands the possible arguments by asking if there are alternative actions (or treatments) available to achieve the same goal. If alternatives are available, then this results in additional instantiations of ASPT with the alternative treatment options.

As mentioned above, our work, being UK-based, made use of NICE guidelines, although we see no reason why guidelines from other countries could not also be used. Below we will illustrate the use of the argumentation schemes introduced above using examples based on the treatment of hypertension. Within the NICE guidelines, the treatment of hypertension involves a patient progressing along a pathway involving the administration of a number of medications. A partial view (it only covers the first step) of the hypertension treatment guideline CG127 published by NICE is shown in Table 12. As shown, there are four possible treatment medications: ACE Inhibitor or low-cost Angiotensin II receptor blocker (ARB), Beta-blocker, calcium-channel blocker (CCB) and thiazide-like Diuretic. In [27], we introduced a formal representation of this guideline in terms of logic-based rules. We will use a subset of these rules here.

We have implemented the argumentation schemes introduced above, along with knowledge from the hypertension guideline. Our code can be accessed from our repository.4

We will show the use of the specialised schemes, introduced in this paper, and the algorithm from [26] through two case studies as described in Section 4. This approach works for those examples, but we acknowledge that further work will be necessary before substantially more complex examples can be tackled, specifically those with larger sets of argument schemes and critical questions. In particular, as we further develop a dialogical formalisation enabling use of the schemes and CQ, attention will need to be paid to defining a strategy function that prioritises the choice of which schemes and CQs to use at any point in the unfolding dialogue. Such a choice is typically encoded in a dialogue protocol’s strategy function. Indeed, such a function could well be informed by doctors, who could help rank the schemes and CQ choices, based on their experience.

The knowledge base includes all the domain-specific information in terms of ASP facts and rules. In the medical context, we consider clinical guidelines being part of the knowledge base. Moreover, each argumentation scheme is translated into a domain-specific rule that generates arguments and attacks automatically as described in [26]. The information in the knowledge base is static, but can be updated at any time to represent the most up-to-date information such as new clinical guidelines or argumentation schemes. A partial view of the knowledge base, as implemented, is shown in Table 13. For simplicity, we only display a formal representation that covers the case studies. The whole knowledge base can be accessed from our repository.

A domain-specific rule is of the form Q :- P, which means that if the antecedent P holds, then the consequent Q also holds. In this work, Q is a singular atom, while P is a conjunction of atoms separated by a comma. The domain-specific rules are grouped together, and each rule has a description above it. There are two specific predicates arg() and att(). The argumentation engine uses these two predicates to construct the argumentation framework including the arguments and the attacks respectively. The patient facts is a set of unary and binary predicates about the patient. This component is dynamic in the sense that the information is extracted from the medical records of the patient, or during the interactions between a patient and a GP. We show an example of this in Section 4.

The Argumentation Schemes proposed herein were developed to support the GP and the patients when deciding on what treatments to consider. In such a situation the decision on what treatment to ultimately prescribe is a dialogue between the GP and the patient external to the argumentation schemes, but supported by the argumentation framework instantiated by the specialised schemes and resulting extensions. In some cases the attacks amongst arguments per. se. may not definitively decide a course of action. In such cases, one can preferentially select amongst arguments on the basis of information that is typically assumed exogenous to the domain of reasoning. This is illustrated in the following Section 4.1 in which two arguments for alternative treatments symmetrically attack, and hence yield two preferred extensions, each of which contain one of the two arguments (in such a case, the grounded extension would of course be empty). One could then appeal to valuations of these arguments, e.g., in terms of cost, or effectiveness, to decide the issue, or indeed, patient and/or GP preferences. However, such preference information may itself be conflicting (e.g., the patient and GP preferences may conflict), and one would want to incorporate reasoning about preference information in the framework. Indeed, this has been explored in work orthogonal to that presented in this paper. On the one hand, we in [28] outline an approach which enables argumentation based reasoning about preferences by extending Dung frameworks to include arguments expressing preferences over other arguments, i.e., in Extended Argumentation Frameworks [34]. On the other hand, in [27] we explore an alternative means of incorporating argumentation based reasoning about possibly conflicting preferences, through the use of Metalevel Argumentation Frameworks [36] that are essentially Dung frameworks that accommodate ‘meta’ arguments expressing preferences over other arguments.

4.1. Case study 1

In this scenario Joey, a 50 year old male patient has suffered a stroke, and has hypertension. For Joey, the goal is to keep his blood pressure under control; and for this, Joey is meeting and discussing treatment options with his GP. This is a simple application of the approach proposed, and it leverages the hypertension guidelines relevant to Step 1.a from Table 13, as this is the first iteration of treatment. For this particular scenario, we represent the patient facts extracted from Joey’s medical record as: suffers_from(joey, hypertension), age(joey, 50) and step(1). The argumentation engine will make recommendations by instantiating the argumentation schemes outlined in this paper, considering the domain-specific rules and the patient facts as described in Section 3.2.

Instantiating ASPT with a goal of reducing blood pressure (rbp), given the known patient facts and clinical guidelines will result in an initial argument in support of the use of ace . This argument is aspt([goal(rbp), action(ace), promotes(ace, rbp)], action(ace)) and is generated as an instantiation of ASPT. The critical questions ASPT.CQ1, ASPT.CQ2 and ASPT.CQ3 do not result in arguments attacking ace . ASPT.CQ4 results in the instantiation of ASPT for alternative treatment, the use of arb . This argument is aspt([goal(rbp), action(arb), promotes(arb, rbp)], action(arb)). In Table 13, we show two rules that represent CQ4-related rules. The first one states how an alternative treatment promoting a same goal becomes an argument. According to the guideline, it is known that ace and arb are two alternative actions that promote the same goal (rbp). Hence, arb becomes an argument. This argument is not attacked by any of its critical questions, for the same reasons as ace . The second rule states that alternative actions should not be prescribed together. In our example, there are two ASPT arguments in support of alternative treatments. Hence, an attack relation is created between these two arguments.

The resulting argumentation framework (AF) is shown in Fig. 3. The resulting AF supports two possible courses of action: taking ace or arb , but not both at the same time, as they are alternative actions. At this stage, we can adopt different semantics to make a recommendation. For example, under preferred semantics [12] this AF will result in two different extensions: the first one promoting the use of ace , and the second one promoting the use of arb . The GP will then make a decision together with Joey as to which of these two treatments to consider. In [28], we discuss how instantiations of argument schemes, preferences and other considerations such as cost can be incorporated into the reasoning.

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Fig. 3.

Argumentation framework for Case study 1.

In this scenario Rachel, a 60 year old female patient has suffered a stroke, and has hypertension. Rachel needs to discuss treatment options with her GP. The known facts about Rachel are: suffers_from(rachel, hypertension), age(rachel, 60) and step(1). The argumentation engine will recommend treatments according to the information in its knowledge base and the patient facts as in the previous case study.

Visit 1. According to the guideline information (Table 12), not all Step 1 treatments are suitable for Rachel because of age restrictions. In other words, the use of ace or arb cannot be recommended. However, according to Step (1.b), ccb is a possible treatment for Rachel. Hence, the generated AF includes the single ASPT argument: aspt([goal(rbp), action(ccb), promotes(ccb, rbp)], action(ccb)), which is the only ASPT argument in support of ccb . Therefore, Rachel is prescribed ccb .

Visit 2. Rachel returns to her GP a few days later complaining of an headache. Rachel and her GP now need to reason about the need to modify the treatment if necessary. The argument schemes are instantiated and now make use of a new additional observation about Rachel observation(headache), and a new fact harmful(headache). Such an observation results in a new inference regarding the knowledge base: nottolerated(ccb) (see Table 13). The resulting AF is shown in Fig. 4. There is a new ASPT argument that supports the use of thiazide . Moreover, the new observation leads to the instantiation of the argumentation scheme ASnegEff. This new argument attacks the ASPT argument in support of the use of ccb . The preferred extension includes the argument based on clinical judgement, as the clinician in this case confirmed that the headache was indeed a side effect of CCB (e.g. a new fact in the KB may_cause(ccb, headache)). In such a case, thiazide can be recommended.

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Fig. 4.

Second AF for the case study: clinician judgement: headache is a side effect.

Contrast this to the case where the clinician does not think that the headache is a side effect of CCB. This input from the clinician instantiates the argument scheme AS_Eff_Cause. Since the clinician thinks that the headache is not because of the use of CCB by Rachel, the justification derived from the knowledge base is not plausible anymore. Therefore, this new argument attacks the argument constructed by the instantiation of the argumentation scheme ASnegEff. This alternative case is depicted in Fig. 5. Here there are two extensions under the preferred semantics because we have two ASPT arguments that attack each other. The GP can recommend thiazide or ccb in this case, which is based on a similar reasoning as we have shown for the previous case study. Moreover, such recommendations can be used to display explanations to the GP (e.g., ccb is a possible treatment option since headache may not be caused by the use of ccb ) [26].

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Fig. 5.

Second AF for the case study: clinician judgement: headache is not a side effect.

The benefit of argumentation in this case is that the rationale for recommending each specific treatment is present, and the process of critical questions ensures that any reasons not to recommend a treatment are checked. The rationale for each of these two arguments is contained within the argument structure, for example in the case of arb then the rationale to support the action(arb) is promotes(arb,rbp). Further explanation templates [26, 48] may be used to exploit the structure of the argument schemes in order to generate a more natural language personalised explanation of the rationale. Applying the approach proposed in [48] to an argument instantiated by ASPT such as aspt([goal(rbp), action(ace), promotes(ace, rbp)], action(ace)) will result in the explanations in Table 14. As part of our future GP evaluation study we plan to evaluate the templates and layouts of the arguments with clinicians.