{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:19:06Z","timestamp":1740201546053,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Finding patient cases with extremely rare pathologies is a laborious task. To decrease time spent on manually searching through thousands of discharge letters and reports, a data warehouse with a fast fulltext search index was queried. Our use case is to find &amp;ldquo;macrofocal myeloma&amp;rdquo;, i.e. Multiple Myeloma patients with few large lesions. We guessed the number of those patients in the University Hospital W&amp;uuml;rzburg at about 20. Most criteria were available in the data warehouse in an unstructured form requiring information extraction. 8 patient cases were found by searching for different spellings of &amp;ldquo;macrofocal myeloma&amp;rdquo; in discharge letters directly. With an indirect search combining several criteria, we found additional 23 candidate patient cases, from which 10 were classified by a domain expert as correct. The most difficult criteria were determining the degree of bone marrow infiltration. We achieved an F1 score of 93.2 % for this task. The number of patient cases to be screened manually for this disease decreased from about 25000 to 23.<\/jats:p>","DOI":"10.3233\/978-1-61499-896-9-160","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:07:16Z","timestamp":1740121636000},"source":"Crossref","is-referenced-by-count":0,"title":["Finding Needles in the Haystack: Identifying Patients with Rare Subtype of Multiple Myeloma Supported by a Data Warehouse and Information Extraction"],"prefix":"10.3233","author":[{"family":"Krebs Jonathan","sequence":"additional","affiliation":[]},{"family":"Bittrich Max","sequence":"additional","affiliation":[]},{"family":"Dietrich Georg","sequence":"additional","affiliation":[]},{"family":"Ertl Maximilian","sequence":"additional","affiliation":[]},{"family":"Fette Georg","sequence":"additional","affiliation":[]},{"family":"Kaspar Mathias","sequence":"additional","affiliation":[]},{"family":"Liman Leon","sequence":"additional","affiliation":[]},{"family":"Einsele Hermann","sequence":"additional","affiliation":[]},{"family":"Puppe Frank","sequence":"additional","affiliation":[]},{"family":"Knop Stefan","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences: A Learning Healthcare System"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:17:13Z","timestamp":1740122233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-895-2&spage=160&doi=10.3233\/978-1-61499-896-9-160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-896-9-160","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}