A semantic approach to involve Twitter in LBD efforts

Literature-based Discovery (LBD) refers to the task of finding hidden, unknown or neglected relationships that may be uncovered using biomedical text. While traditional LBD primarily focuses on MEDLINE records for unearthing such relationships, recent studies have shown the applicability of contemporary textual resources such as electronic medical records or online medical message boards for similar purposes. In this paper we highlight yet another source for LBD, i.e., Twitter data. We focus on the use of Twitter as a new resource for finding hypotheses - both novel and slightly studied. Using a set of drug and disease names as starting points we retrieve thousands of Twitter messages which are then processed for semantic information to mine several hundred biomedical relationships which we call probes. Manual inspection of a handful of these probes reveals instances where tweets strongly support a hypothesis for which no evidence can be found in PubMed. In other cases, we find very few related PubMed records supporting/rejecting such Twitter-mined probes. Overall, we show the importance and usefulness of Twitter for LBD efforts.

[1]  Padmini Srinivasan,et al.  Text mining: Generating hypotheses from MEDLINE , 2004, J. Assoc. Inf. Sci. Technol..

[2]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[3]  Xiaohua Hu.  Mining novel connections from large online digital library using biomedical ontologies , 2005 .

[4]  Xiaofeng Wang,et al.  Mining hidden connections among biomedical concepts from disjoint biomedical literature sets through semantic-based association rule , 2010 .

[5]  Ronald N. Kostoff,et al.  Literature-related discovery (LRD): Methodology , 2008 .

[6]  Carol Friedman,et al.  Exploiting Semantic Relations for Literature-Based Discovery , 2006, AMIA.

[7]  Alla Keselman,et al.  Term Identification Methods for Consumer Health Vocabulary Development , 2007, Journal of medical Internet research.

[8]  Ronald N. Kostoff,et al.  Validating discovery in literature-based discovery , 2007, J. Biomed. Informatics.

[9]  Wanda Pratt,et al.  Using statistical and knowledge-based approaches for literature-based discovery , 2006, J. Biomed. Informatics.

[10]  Søren Brunak,et al.  Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts , 2011, PLoS Comput. Biol..

[11]  Neil R. Smalheiser,et al.  Artificial Intelligence An interactive system for finding complementary literatures : a stimulus to scientific discovery , 1995 .

[12]  Lawrence Hunter,et al.  Extracting Molecular Binding Relationships from Biomedical Text , 2000, ANLP.

[13]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[14]  D. Swanson.  Undiscovered Public Knowledge , 1986 .

[15]  Thomas C. Rindflesch,et al.  Semantic Processing for Enhanced Access to Biomedical Knowledge , 2002 .

[16]  D. Swanson.  Somatomedin C and Arginine: Implicit Connections between Mutually Isolated Literatures , 2015, Perspectives in biology and medicine.

[17]  Padmini Srinivasan,et al.  Belief surveillance with Twitter , 2012, WebSci '12.

[18]  D. Swanson.  Migraine and Magnesium: Eleven Neglected Connections , 2015, Perspectives in biology and medicine.

[19]  Margo I. Seltzer,et al.  Mining the Web for Medical Hypotheses - A Proof-of-Concept System , 2012, HEALTHINF.

[20]  R. N. Kostoff,et al.  Where is the Discovery in Literature-Based Discovery? , 2008 .

[21]  Lyle H. Ungar,et al.  Identifying potential adverse effects using the web: A new approach to medical hypothesis generation , 2011, J. Biomed. Informatics.

[22]  T C Rindflesch,et al.  Semantic processing in information retrieval. , 1993, Proceedings. Symposium on Computer Applications in Medical Care.

[23]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.