A visual approach to explainable computerized clinical decision support
| Autor: | Alexander Oeser, Jan Gaebel, Steffen Oeltze-Jafra, Marc Streit, Andreas Dietz, Matthaeus Stoehr, Juliane Müller |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Knowledge management
Computer science business.industry media_common.quotation_subject General Engineering Bayesian network 020207 software engineering 02 engineering and technology Certainty Computer Graphics and Computer-Aided Design Clinical decision support system Transparency (behavior) Textual information Clinical knowledge Human-Computer Interaction Visual approach 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Multiple view media_common |
| Zdroj: | Computers & Graphics. 91:1-11 |
| ISSN: | 0097-8493 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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