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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005463naa a2200493 4500
001oai:DiVA.org:oru-56621
003SwePub
008170321s2013 | |||||||||||000 ||eng|
009oai:prod.swepub.kib.ki.se:129059078
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-566212 URI
0247 a https://doi.org/10.1136/amiajnl-2013-0019242 DOI
0247 a http://kipublications.ki.se/Default.aspx?queryparsed=id:1290590782 URI
040 a (SwePub)orud (SwePub)ki
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Ludvigsson, Jonas F.u Karolinska Institutet,Region Örebro län,Dept Pediat, Örebro University Hospital, Örebro, Sweden; Dept Med, Clin Epidemiol Unit, Karolinska Instute, Stockholm, Sweden; Dept Med, Div Gastroenterol & Hepatol, Coll Med, Rochester MN, USA; Dept Immunol, Div Gastroenterol & Hepatol, Coll Med, Mayo Clin, Rochester MN, USA4 aut0 (Swepub:oru)jsln
2451 0a Use of computerized algorithm to identify individuals in need of testing for celiac disease
264 c 2013-12-01
264 1b Oxford University Press (OUP),c 2013
338 a print2 rdacarrier
520 a Background and aim Celiac disease (CD) is a lifelong immune-mediated disease with excess mortality. Early diagnosis is important to minimize disease symptoms, complications, and consumption of healthcare resources. Most patients remain undiagnosed. We developed two electronic medical record (EMR)-based algorithms to identify patients at high risk of CD and in need of CD screening. Methods (I) Using natural language processing (NLP), we searched EMRs for 16 free text (and related) terms in 216 CD patients and 280 controls. (II) EMRs were also searched for ICD9 (International Classification of Disease) codes suggesting an increased risk of CD in 202 patients with CD and 524 controls. For each approach, we determined the optimal number of hits to be assigned as CD cases. To assess performance of these algorithms, sensitivity and specificity were calculated. Results Using two hits as the cut-off, the NLP algorithm identified 72.9% of all celiac patients (sensitivity), and ruled out CD in 89.9% of the controls (specificity). In a representative US population of individuals without a prior celiac diagnosis (assuming that 0.6% had undiagnosed CD), this NLP algorithm could identify a group of individuals where 4.2% would have CD (positive predictive value). ICD9 code search using three hits as the cut-off had a sensitivity of 17.1% and a specificity of 88.5% (positive predictive value was 0.9%). Discussion and conclusions This study shows that computerized EMR-based algorithms can help identify patients at high risk of CD. NLP-based techniques demonstrate higher sensitivity and positive predictive values than algorithms based on ICD9 code searches.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Allmänmedicin0 (SwePub)302242 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex General Practice0 (SwePub)302242 hsv//eng
650 7a SAMHÄLLSVETENSKAPx Medie- och kommunikationsvetenskapx Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning0 (SwePub)508042 hsv//swe
650 7a SOCIAL SCIENCESx Media and Communicationsx Information Systems, Social aspects0 (SwePub)508042 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Gastroenterologi0 (SwePub)302132 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Gastroenterology and Hepatology0 (SwePub)302132 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi0 (SwePub)303012 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Health Care Service and Management, Health Policy and Services and Health Economy0 (SwePub)303012 hsv//eng
7001 a Pathak, Jyotishmanu Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Murphy, Seanu Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Durski, Matthewu Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Kirsch, Phillip S.u Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Chute, Christophe G.u Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Ryu, Euijungu Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7001 a Murray, Joseph A.u Dept Med, Div Gastroenterol & Hepatol, Coll Med, Mayo Clin, Rochester MN, USA; Coll Med, Dept Hlth Sci, Mayo Clin, Rochester MN, USA4 aut
7102 a Region Örebro länb Dept Pediat, Örebro University Hospital, Örebro, Sweden; Dept Med, Clin Epidemiol Unit, Karolinska Instute, Stockholm, Sweden; Dept Med, Div Gastroenterol & Hepatol, Coll Med, Rochester MN, USA; Dept Immunol, Div Gastroenterol & Hepatol, Coll Med, Mayo Clin, Rochester MN, USA4 org
7730 t JAMIA Journal of the American Medical Informatics Associationd : Oxford University Press (OUP)g 20:E2, s. E306-E310q 20:E2<E306-E310x 1067-5027x 1527-974X
8564 u https://academic.oup.com/jamia/article-pdf/20/e2/e306/6114220/20-e2-e306.pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-56621
8564 8u https://doi.org/10.1136/amiajnl-2013-001924
8564 8u http://kipublications.ki.se/Default.aspx?queryparsed=id:129059078

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