Argumentation in Decision Support for Medical Care Planning for Patients and Clinicians.

Developing a care plan for a patient requires an understanding of interactions and dependencies between procedures, and of their possible outcomes for an individual patient, and it requires the planner to keep track of this information as the proposed plan evolves. This is difficult even for experienced clinicians, but increasingly patients are expected (and expect) to participate. We describe an argumentation-based planning support system designed to ameliorate the cognitive load imposed by the planning and communication elements of such tasks. An initial evaluation study in the field of genetic counseling produced promising results. The approach may provide a general aid for clinicians and patients in visualizing, customizing, evaluating and communicating about care plans

Argumentation Schemes for Clinical Decision Support

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created, and illustrates the overall process on a small set of case studies

ProCLAIM: an argument-based model for deliberating over safety critical actions

In this Thesis we present an argument-based model – ProCLAIM – intended to provide a setting for heterogeneous agents to deliberate on whether a proposed action is safe. That is, whether or not a proposed action is expected to cause some undesirable side effect that will justify not to undertake the proposed action. This is particularly relevant in safetycritical environments where the consequences ensuing from an inappropriate action may be catastrophic. For the practical realisation of the deliberations the model features a mediator agent with three main tasks: 1) guide the participating agents in what their valid argumentation moves are at each stage of the deliberation; 2) decide whether submitted arguments should be accepted on the basis of their relevance; and finally, 3) evaluate the accepted arguments in order to provide an assessment on whether the proposed action should or should not be undertaken, where the argument evaluation is based on domain consented knowledge (e.g guidelines and regulations), evidence and the decision makers’ expertise. To motivate ProCLAIM’s practical value and generality the model is applied in two scenarios: human organ transplantation and industrial wastewater. In the former scenario, ProCLAIM is used to facilitate the deliberation between two medical doctors on whether an available organ for transplantation is or is not suitable for a particular potential recipient (i.e. whether it is safe to transplant the organ). In the later scenario, a number of agents deliberate on whether an industrial discharge is environmentally safe.En esta tesis se presenta un modelo basado en la Argumentación –ProCLAIM– cuyo n es proporcionar un entorno para la deliberación sobre acciones críticas para la seguridad entre agentes heterogéneos. En particular, el propósito de la deliberación es decidir si los efectos secundario indeseables de una acción justi can no llevarla a cabo. Esto es particularmente relevante en entornos críticos para la seguridad, donde las consecuencias que se derivan de una acción inadecuada puede ser catastró cas. Para la realización práctica de las deliberaciones propuestas, el modelo cuenta con un agente mediador con tres tareas principales: 1) guiar a los agentes participantes indicando cuales son las líneas argumentación válidas en cada etapa de la deliberación; 2) decidir si los argumentos presentados deben ser aceptadas sobre la base de su relevancia y, por último, 3) evaluar los argumentos aceptados con el n de proporcionar una valoración sobre la seguridad de la acción propuesta. Esta valoración se basa en guías y regulaciones del dominio de aplicación, en evidencia y en la opinión de los expertos responsables de la decisión. Para motivar el valor práctico y la generalidad de ProCLAIM, este modelo se aplica en dos escenarios distintos: el trasplante de órganos y la gestión de aguas residuales. En el primer escenario el modelo se utiliza para facilitar la deliberación entre dos médicos sobre la viabilidad del transplante de un órgano para un receptor potencial (es decir, si el transplante es seguro). En el segundo escenario varios agentes deliberan sobre si los efectos de un vertido industrial con el propósito de minimizar su impacto medioambiental

Persuasive and adaptive tutorial dialogues for a medical diagnosis tutoring system

The objective of this thesis is to address a key problem in the development of an intelligent tutoring system, that is, the implementation of the verbal exchange (a dialogue) that takes place between a student and the system. Here we consider TeachMed, a medical diagnosis tutoring system that teaches the students to diagnose clinical problems. However, approaches that are presented could also fit other tutoring systems. In such a system, a dialogue must be implemented that determines when and how pedagogic aid is provided to the student, that is, what to say to her, in what circumstances, and how to say it. Finite state machines and automated planning systems are so far the two most common approaches for implementing tutoring dialogues in intelligent tutoring systems. In the former approach, finite state machines of dialogues are manually designed and hard coded in intelligent tutoring systems. This is a straightforward but very time consuming approach. Furthermore, any change or extension to the hard coded finite state machines is very difficult as it requires reprogramming the system. On the other hand, automated planning has long been presented as a promising technique for automatic dialogue generating. However, in existing approaches, the requirement for the system to persuade the student is not formally acknowledged. Moreover, current dialogue planning approaches are not able to reason on uncertainties about the student's knowledge. This thesis presents two approaches for generating more effective tutorial dialogues.The first approach describes an argumentation framework for implementing persuasive tutoring dialogues. In this approach the entire interaction between the student and the tutoring system is seen as argumentation.The tutoring system and the student can settle conflicts arising during their argumentation by accepting, challenging, or questioning each other's arguments or withdrawing their own arguments. Pedagogic strategies guide the tutoring system by selecting arguments aimed at convincing the student.The second approach presents a non-deterministic planning technique which models the dialogue generation problem as one of planning with incomplete knowledge and sensing. This approach takes into account incomplete information about a particular fact of the student's knowledge by creating conditional branches in a dialogue plan such that each branch represents an adaptation of the dialogue plan with respect to a particular state of the student's knowledge or belief concerning the desired fact. In order to find out the real state of the student's knowledge and to choose the right branch at execution time, the planner includes some queries in the dialogue plan so that the tutoring system can ask the student to gather missing information. One contribution in this thesis is improving the quality of tutoring dialogues by engaging students in argumentative interactions and/or adapting the dialogues with respect to the student's knowledge. Another one is facilitating the design and implementation of tutoring by turning to automatically generated dialogues as opposed to manually generated ones

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis