Specialised tools are needed when searching the web for rare disease diagnoses

In our recent paper, we study web search as an aid in the process of diagnosing rare diseases. To answer the question of how well Google Search and PubMed perform, we created an evaluation framework with 56 diagnostic cases and made our own specialized search engine, FindZebra (findzebra.com). FindZebra uses a set of publicly available curated sources on rare diseases and an open-source information retrieval system, Indri. Our evaluation and the feedback received after the publication of our paper both show that FindZebra outperforms Google Search and PubMed. In this paper, we summarize the original findings and the response to FindZebra, discuss why Google Search is not designed for specialized tasks and outline some of the current trends in using web resources and social media for medical diagnosis

FindZebra:a search engine for rare diseases

BACKGROUND: The web has become a primary information resource about illnesses and treatments for both medical and non-medical users. Standard web search is by far the most common interface to this information. It is therefore of interest to find out how well web search engines work for diagnostic queries and what factors contribute to successes and failures. Among diseases, rare (or orphan) diseases represent an especially challenging and thus interesting class to diagnose as each is rare, diverse in symptoms and usually has scattered resources associated with it. METHODS: We design an evaluation approach for web search engines for rare disease diagnosis which includes 56 real life diagnostic cases, performance measures, information resources and guidelines for customising Google Search to this task. In addition, we introduce FindZebra, a specialized (vertical) rare disease search engine. FindZebra is powered by open source search technology and uses curated freely available online medical information. RESULTS: FindZebra outperforms Google Search in both default set-up and customised to the resources used by FindZebra. We extend FindZebra with specialized functionalities exploiting medical ontological information and UMLS medical concepts to demonstrate different ways of displaying the retrieved results to medical experts. CONCLUSIONS: Our results indicate that a specialized search engine can improve the diagnostic quality without compromising the ease of use of the currently widely popular standard web search. The proposed evaluation approach can be valuable for future development and benchmarking. The FindZebra search engine is available at http://www.findzebra.com/

Considerations for the Development of Task-Based Search Engines

Based on previous experience from working on a task-based search engine, we present a list of suggestions and ideas for an Information Retrieval (IR) framework that could inform the development of next generation professional search systems. The specific task that we start from is the clinicians ’ information need in finding rare disease diagnostic hypotheses at the time and place where medical decisions are made. Our experience from the development of a search engine focused on supporting clinicians in completing this task has provided us valuable insights in what aspects should be considered by the developers of vertical search engines.

Rare disease diagnosis as an information retrieval task

Abstract. Increasingly more clinicians use web Information Retrieval (IR) systems to assist them in diagnosing dicult medical cases, for in-stance rare diseases that they may not be familiar with. However, web IR systems are not necessarily optimised for this task. For instance, clinicians ’ queries tend to be long lists of symptoms, often containing phrases, whereas web IR systems typically expect very short keyword-based queries. Motivated by such di↵erences, this work uses a prelimi-nary study of 30 clinical cases to reflect on rare disease retrieval as an IR task. Initial experiments using both Google web search and o✏ine retrieval from a rare disease collection indicate that the retrieval of rare diseases is an open problem with room for improvement