Explanation Oriented Retrieval

Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 1999

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation.

Lecture Notes in Computer Science, 1994

Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method -captured in what is called the ACTIVATE-EXPLAIN-FOCUS cycle -is able to utilise a rich knowledge model in producing contextdependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples.

Computational …, 2006

The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners.

PLOS Digital Health

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the sy...

10.1088/1742-6596/1003/1/012130, 2018

View the article online for updates and enhancements. You may also like Case-Based Reasoning for the Diagnosis of Acute Respiratory Infections Using Minkowski Distance M Salmin, F Tempola, A Fuad et al. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

2020

We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.

Artificial Intelligence Review, 2005

Most users of machine-learning products are reluctant to use the systems without any sense of the underlying logic that has led to the system's predictions. Unfortunately many of these systems lack any transparency in the way they operate and are deemed to be 'black boxes'. In this paper we present a Case-Based Reasoning (CBR) solution to providing supporting explanations of black-box systems. This CBR solution uses locally derived feature ranking information that reflects the importance of each feature to a prediction and a locally adjusted case retrieval mechanism. The retrieval mechanism takes advantage of the derived feature weightings to help select cases that are a better reflection of the black-box solution and thus more convincing explanations.

Case-Based Reasoning Research and …, 2005

Artificial Intelligence Review, 2005

WM, 2005