Gangl, C., Lackner, M., Maly, J., & Woltran, S. (2019). Aggregating Expert Opinions in Support of Medical Diagnostic Decision-Making. In Knowledge Representation for Health Care/ProHealth, KR4HC 2019 (pp. 56–62). http://hdl.handle.net/20.500.12708/57907
E192-02 - Forschungsbereich Databases and Artificial Intelligence
Knowledge Representation for Health Care/ProHealth, KR4HC 2019
KR4HC 2019 - Knowledge Representation for Health Care/ProHealth
26-Jun-2019 - 29-Jun-2019
Poznan, Polen, EU
Number of Pages:
Medical Decision Support · Computational Social Choice · Preference Aggregation · Preference Elicitation · Multi-Winner Voting
Medical doctors are often faced with challenging diagnostic decisions, which require the consideration of all eligible differential diagnoses. Diagnostic decisions (i.e., which test to order next in a given situation) have a high impact, as non-targeted diagnostic strategies may cause delayed treatments. It is thus desirable for medical professionals to be able to tap into the knowledge from more experienced colleagues - a process which can be fostered and supported by knowledge-based software tools. In this position paper, we outline the potential and challenges of applying methods from Computational Social Choice (COMSOC) to aggregate expert advice on diagnostic strategies.
Gathering and aggregating expert opinions is a challenging task, especially in the medical domain. We discuss the necessary requirements for COMSOC methods to be applicable in the diagnostic support setting, in particular requirements for opinion elicitation and opinion aggregation. The main goal of our research is to build a system that supports diagnostic decision-making based on reliable expert knowledge. Principled methods and analyses from COMSOC guarantee that recommendations are reliable, sound, and explainable.
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