Argumentation-logic for creating and explaining medical hypotheses

Grando, Adela; Moss, Laura; Sleeman, Derek; Kinsella, John

doi:10.1016/j.artmed.2013.02.003

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…Dung semantics (Dung, 1995) and its variations (Caminada, 2007;Caminada et al, 2012) are the most well known. Other types include SCC-recursive semantics (Baroni et al, 2005) focused on solving cyclic attack relations of odd-length and ranking-based semantics (Bonzon et al, 2016) Some works tackle all these 5 layers (Chang et al, 2009;Hunter & Williams, 2010;Craven et al, 2012) while others do not (Patkar et al, 2006;Glasspool et al, 2006;Grando et al, 2013). This structure has also been reproduced in past studies (Rizzo & Longo, 2017;Rizzo et al, 2018a;Longo, 2015;Rizzo & Longo, 2018) demonstrating structural effectiveness in different domains of application.…”

Section: Defeasible Argumentationmentioning

confidence: 66%

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

Rizzo

Longo

2020

Expert Systems with Applications

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Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined

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Section: Defeasible Argumentationmentioning

confidence: 66%

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

Rizzo

Longo

2020

Expert Systems with Applications

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“…Studies have shown marked similarities in the way statisticians decide which model to use and how clinicians diagnose a patient [13], as such applications of argumentation for clinical decision support are of particular relevance to our work. EIRA (Explaining, Inferencing, and Reasoning about Anomalies) is an argumentation-based clinical decision support system designed to flag anomalies in patients' reactions to medication within the Intensive Care Unit [11,12]. Grando et al describe EIRA as a hypothesis generation tool that leverages Argumentation Schemes and Dung's argumentation frameworks.…”

Section: Related Workmentioning

confidence: 99%

“…The evaluation of methodologies and prototypes leveraging argumentation for clinical decision support has been through case studies and user studies. ArguEIRA [12] was evaluated by clinicians assessing the tool's output, CARREL [28] was similarly evaluated on a set of examples as well as DRAMA [2,3] where examples were also used to ensure the proposed argument scheme and knowledge base were comprehensive enough. The initial evaluation approach we took is similar in nature as it is initially case study based.…”

Section: Related Workmentioning

confidence: 99%

A formalisation and prototype implementation of argumentation for statistical model selection

Sassoon

Zillessen²,

Keppens

et al. 2018

AAC

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The task of data collection is becoming routine in many disciplines and this results in increased availability of data. This routinely collected data provides a valuable opportunity for analysis with a view to support evidence based decision making. In order to confidently leverage the data in support of decision making the most appropriate statistical method needs to be selected, and this can be difficult for an end user not trained in statistics. This paper outlines an application of argumentation to support the analysis of clinical data, that uses Extended Argumentation Frameworks in order to reason with the metalevel arguments derived from preference contexts relevant to the data and the analysis objective of the end user. We outline a formalisation of the argument scheme for statistical model selection, its critical questions and the structure of the knowledge base required to support the instantiation of the arguments and meta-level arguments through the use of Z notation. This paper also describes the prototype implementation of argumentation for statistical model selection based on the Z specification outlined herein.

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“…Dialogue and argumentation can play an important role in addressing these issues [20,31,48], especially in e-health systems designed around health coaching [30,52]. Advice and support that is based on sound models of reasoning and delivered through dialogue-based interaction can simultaneously deliver a high level of user experience and engagement (through, for example, mixed-initiative dialogues [53] with multiple agent actors providing the advice), while also ensuring that legal and ethical standards are adhered to.…”

Section: Introductionmentioning

confidence: 99%

Ethical challenges in argumentation and dialogue in a healthcare context

Snaith

Nielsen

Kotnis

et al. 2021

AAC

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As the average age of the population increases, so too do the number of people living with chronic illnesses. With limited resources available, the development of dialogue-based e-health systems that provide justified general health advice offers a cost-effective solution to the management of chronic conditions. It is however imperative that such systems are responsible in their approach. We present in this paper two main challenges for the deployment of e-health systems, that have a particular relevance to dialogue and argumentation: collecting and handling health data, and trust. For both challenges, we look at specific issues therein, outlining their importance in general, and describing their relevance to dialogue and argumentation. Finally, we go on to propose six recommendations for handling these issues, towards addressing the main challenges themselves, that act both as general advice for dialogue and argumentation research in the e-health domain, and as a foundation for future work on this topic.

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Argumentation-logic for creating and explaining medical hypotheses

Cited by 17 publications

References 14 publications

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

A formalisation and prototype implementation of argumentation for statistical model selection

Ethical challenges in argumentation and dialogue in a healthcare context

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