PatientsLikeMe: Consumer Health Vocabulary as a Folksonomy. Export

@article{citeulike:3512181, abstract = {PatientsLikeMe is an online social networking community. Subcommunities center on three diagnoses: Amyotrophic Lateral Sclerosis, Multiple Sclerosis and Parkinsons Disease. Community members can describe their symptoms online in natural language, resulting in folksonomic tags available for clinical analysis and for browsing by other users to find patients like me. Forty-three percent of PatientsLikeMe symptom terms are present as exact (24\%) or synonymous (19\%) terms in the Unified Medical Language System Metathesaurus (National Library of Medicine; 2007AC). Slightly more than half of the symptom terms either do not match the UMLS, or are unclassifiable. A clinical vocabulary, SNOMED CT, accounts for 93\% of the matching terms. Analysis of the failed matches reveals challenges for online patient communication, not only with healthcare professionals, but with other patients. In a Web 2.0 environment with lowered barriers between consumers and professionals, a deficiency in knowledge representation affects not only professionals, but consumers as well.}, author = {Smith, Catherine Arnott A. and Wicks, Paul J.}, citeulike-article-id = {3512181}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/18999004}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=18999004}, issn = {1942-597X}, journal = {AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium}, keywords = {patients}, pages = {682--686}, posted-at = {2009-02-11 21:27:19}, priority = {2}, title = {PatientsLikeMe: Consumer Health Vocabulary as a Folksonomy.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18999004}, year = {2008} }

TY - JOUR ID - citeulike:3512181 L3 - citeulike-article-id:3512181 N2 - PatientsLikeMe is an online social networking community. Subcommunities center on three diagnoses: Amyotrophic Lateral Sclerosis, Multiple Sclerosis and Parkinsons Disease. Community members can describe their symptoms online in natural language, resulting in folksonomic tags available for clinical analysis and for browsing by other users to find patients like me. Forty-three percent of PatientsLikeMe symptom terms are present as exact (24%) or synonymous (19%) terms in the Unified Medical Language System Metathesaurus (National Library of Medicine; 2007AC). Slightly more than half of the symptom terms either do not match the UMLS, or are unclassifiable. A clinical vocabulary, SNOMED CT, accounts for 93% of the matching terms. Analysis of the failed matches reveals challenges for online patient communication, not only with healthcare professionals, but with other patients. In a Web 2.0 environment with lowered barriers between consumers and professionals, a deficiency in knowledge representation affects not only professionals, but consumers as well. JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium SN - 1942-597X EP - 686 TI - PatientsLikeMe: Consumer Health Vocabulary as a Folksonomy. SP - 682 KW - patients AU - Smith, Catherine Arnott AU - Wicks, Paul PY - 2008/// UR - http://view.ncbi.nlm.nih.gov/pubmed/18999004 ER -