Argumentation about Treatment Efficacy

Gorogiannis, Nikos; Hunter, Anthony; Patkar, Vivek; Williams, Matthew

doi:10.1007/978-3-642-11808-1_14

Cited by 7 publications

(5 citation statements)

References 6 publications

(7 reference statements)

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“…Moreover, it would be helpful to have a method for automatically analyzing and presenting the clinical trial results and the possible ways to aggregate those in an intuitive form, highlighting agreement and conflict present within the literature. As a first step to addressing these needs, our proposal in [3], [1], [2] presents a general framework for representing and synthesizing knowledge from clinical trials involving the same outcome indicator (e.g. overall survival, or disease-free survival).…”

Section: Introductionmentioning

confidence: 99%

Argumentation for Aggregating Clinical Evidence

Hunter

Williams

2010

2010 22nd IEEE International Conference on Tools With Artificial Intelligence

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Evidence-based decision making is becoming increasingly important in healthcare. Much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. For this, in previous papers [1], [2], we have proposed a general framework for representing and synthesizing knowledge from clinical trials involving the same outcome indicator. Now, in this paper, we present a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators. In this framework, evidence from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc., comparing a pair of treatments τ1 and τ2 according to desired and/or undesired outcomes is aggregated to give an overall evaluation of the treatments saying τ1 is superior to τ2, or τ1 is equivalent to τ2, or τ1 is inferior to τ2. Our general framework incorporates inference rules for generating arguments and counterarguments for claiming that one treatment is superior to another based on the available evidence, and preference rules for specifying which arguments are preferred. In this paper, we also present a new version of this framework that incorporates utility-theoretic criteria for defining specific preference rules over arguments.

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Section: Introductionmentioning

confidence: 99%

Argumentation for Aggregating Clinical Evidence

Hunter

Williams

2010

2010 22nd IEEE International Conference on Tools With Artificial Intelligence

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“…In [59], rules were used to combine treatments for multimorbidity, and in [19] clinical guideline expressions were encoded in GELLO. Authors in [57] defined patterns of eligibility criteria for clinical trials that cover most criteria found in clinical guidelines and [40] proposed a language to capture and synthesize the knowledge generated in such trials. Knowledge in clinical guidelines were also represented in first order logic [74] or combined under a mathematical model, in order to deal with comorbidity [72].…”

Section: Knowledge Specificationmentioning

confidence: 99%

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Riaño

Peleg

Teije

2019

Artificial Intelligence in Medicine

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Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. Methods: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. Results: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. Conclusions: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patie...

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“…Our proposal in [9,8] aims to suggest such a method. The first part of our proposal is a language that can be used to encode the published results in a semantically appropriate way, and methods for constructing a knowledge base from the encoded results.…”

Section: Introductionmentioning

confidence: 99%

Using clinical preferences in argumentation about evidence from clinical trials

Hunter

Williams

2010

Proceedings of the 1st ACM International Health Informatics Symposium

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Medical practice is increasingly based on the best available evidence, but the volume of information requires many clinicians to rely on systematic reviews rather than the primary evidence. However, these reviews are difficult to maintain, and often do not appear transparent to clinicians reading them. In a previous paper [8], we have proposed a general language for representing knowledge from clinical trials and a framework that allows reasoning with that knowledge in order to construct and evaluate arguments and counterarguments that aggregate that knowledge. However, clinicians need to feel that such a framework is responsive to their assessment of the strengths and weaknesses of different types of evidence. In this paper, we use a specific version of this existing framework to show how we can capture clinical preferences over types of evidence, and we evaluate this in a pilot study, comparing our system against the choices made by clinicians. This pilot study shows how individual clinicians aggregate evidence based on their preferences over the relative significance of the items of evidence, and it shows how our argumentation system can replicate this behaviour.

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Argumentation about Treatment Efficacy

Cited by 7 publications

References 6 publications

Argumentation for Aggregating Clinical Evidence

Argumentation for Aggregating Clinical Evidence

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Using clinical preferences in argumentation about evidence from clinical trials

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