Task-based search addresses situations where standard off-the-shelf Information Retrieval (IR) technology may not suffice to satisfy users in their tasks. In these situations, IR systems should be tailored to the user’s task-specific needs and requirements. One such task is searching for rare disease diagnostic hypotheses in the domain of medical IR.

In this work, we build upon an existing vertical medical search engine, Zebra, that is focused on rare disease diagnosis. In previous work, Zebra has been evaluated using real-life medical cases of rare and difficult diseases, and has been found to be a useful and competitive tool for clinicians. In this work, we extend Zebra’s functionalities to optimise the task of medical diagnosis through search as follows: we add the option of grouping retrieved documents into clusters based on disease name occurrence, and we offer a ‘disease-ranking’ option, in addition to the standard ‘document-ranking’ option. This paper presents and discusses these functionalities.