title: Rare disease diagnosis: A review of web search, social media and large-scale data-mining approaches.
abstract: Physicians and the general public are increasingly using web-based tools to find answers to medical questions. The field of rare diseases is especially challenging and important as shown by the long delay and many mistakes associated with diagnoses. In this paper we review recent initiatives on the use of web search, social media and data mining in data repositories for medical diagnosis. We compare the retrieval accuracy on 56 rare disease cases with known diagnosis for the web search tools google.com, pubmed.gov, omim.org and our own search tool findzebra.com. We give a detailed description of IBM's Watson system and make a rough comparison between findzebra.com and Watson on subsets of the Doctor's dilemma dataset. The recall@10 and recall@20 (fraction of cases where the correct result appears in top 10 and top 20) for the 56 cases are found to be be 29%, 16%, 27% and 59% and 32%, 18%, 34% and 64%, respectively. Thus, FindZebra has a significantly (p < 0.01) higher recall than the other 3 search engines. When tested under the same conditions, Watson and FindZebra showed similar recall@10 accuracy. However, the tests were performed on different subsets of Doctors dilemma questions. Advances in technology and access to high quality data have opened new possibilities for aiding the diagnostic process. Specialized search engines, data mining tools and social media are some of the areas that hold promise.
authors.affiliation: DTU Compute; Technical University ; Lyngby, Denmark.
authors.forename: Dan
authors.initials: D
authors.lastname: Svenstrup
authors.affiliation: Department of Clinical Biochemistry; Bispebjerg Hospital ; Copenhagen, Denmark.
authors.forename: Henrik L
authors.initials: HL
authors.lastname: Jørgensen
authors.affiliation: DTU Compute; Technical University ; Lyngby, Denmark.
authors.forename: Ole
authors.initials: O
authors.lastname: Winther
keywords: clinical diagnosis decision support systems; data mining; information retrieval; machine learning; rare diseases; search engines