{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:25:07Z","timestamp":1758709507458,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684000"},{"type":"electronic","value":"9781643684017"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>In our recent study, the attempt to classify neurosurgical operative reports into routinely used expert-derived classes exhibited an F-score not exceeding 0.74. This study aimed to test how improving the classifier (target variable) affected the short text classification with deep learning on real-world data. We redesigned the target variable based on three strict principles when applicable: pathology, localization, and manipulation type. The deep learning significantly improved with the best result of operative report classification into 13 classes (accuracy = 0.995, F1 = 0.990). Reasonable text classification with machine learning should be a two-way process: the model performance must be ensured by the unambiguous textual representation reflected in corresponding target variables. At the same time, the validity of human-generated codification can be inspected via machine learning.<\/jats:p>","DOI":"10.3233\/shti230508","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:53:22Z","timestamp":1688111602000},"source":"Crossref","is-referenced-by-count":1,"title":["Data Quality Estimation Via Model Performance: Machine Learning as a Validation Tool"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1442-5993","authenticated-orcid":false,"given":"Gleb","family":"Danilov","sequence":"first","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Kotik","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Shifrin","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulia","family":"Strunina","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatiana","family":"Pronkina","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatiana","family":"Tsukanova","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Nepomnyashiy","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolay","family":"Konovalov","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valeriy","family":"Danilov","sequence":"additional","affiliation":[{"name":"Kazan State Medical University, Kazan, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Potapov","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230508","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:53:23Z","timestamp":1688111603000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230508","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}