A visual approach to explainable computerized clinical decision support. (October 2020)

• Record Type: Journal Article
• Authors: Müller, Juliane; Stoehr, Matthaeus; Oeser, Alexander; Gaebel, Jan; Streit, Marc; Dietz, Andreas; Oeltze-Jafra, Steffen
• Is Part Of: Computers & graphics. Volume 91(2020)
• Page Start: 1
• Page End: 11
• Publication Date: 2020-10
• Subjects: Information systems (hypertext navigation, interfaces, decision-support, etc.) -- Applications to biology and medical sciences -- Medical applications (general); Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2020.06.004
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: Visual approach for explainable clinical decision support inspired by decision making within clinical routine. Scoring function for computing the relevance of an evidence item for the recommendation. Classification of evidence items into supportive, contradictory, and recommendation changers. Capabilities to modify evidence items with a comparative glyph-based visualization of anterior and posterior results. Graphical abstract: Abstract: Clinical Decision Support Systems (CDSS) provide assistance to physicians in clinical decision-making. Based on patient-specific evidence items triggering the inferencing process, such as examination findings, and expert-modeled or machine-learned clinical knowledge, these systems provide recommendations in finding the right diagnosis or the optimal therapy. The acceptance of, and the trust in, a CDSS are highly dependent on the transparency of the recommendation's generation. Physicians must know both the key influences leading to a specific recommendation and the contradictory facts. They must also be aware of the certainty of a recommendation and its potential alternatives. We present a glyph-based, interactive multiple views approach to explainable computerized clinical decision support. Four linked views (1) provide a visual summary of all evidence items and their relevance for the computation result, (2) present linked textual information, such as clinical guidelines or therapy details, (3) show the certainty of the computation result, which includes the recommendation and a set of clinical scores, stagings etc., and (4) facilitate a guided investigation of the reasoning behind the recommendation generation as well as convey the effect of updated evidence items. We demonstrate our approach for a CDSS based on a causal Bayesian network representing the therapy of laryngeal cancer. The approach has been developed in close collaboration with physicians, and was assessed by six expert otolaryngologists as being tailored to physicians' needs in understanding a CDSS.