{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T10:48:30Z","timestamp":1689763710160},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"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":[[2022,5,25]]},"abstract":"<jats:p>Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance.<\/jats:p>","DOI":"10.3233\/shti220609","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:57Z","timestamp":1653481077000},"source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning-Based Brain Hemorrhage Detection in CT Reports"],"prefix":"10.3233","author":[{"given":"G\u0131yaseddin","family":"Bayrak","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Marmara University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammed \u015eakir","family":"Toprak","sequence":"additional","affiliation":[{"name":"Ministry of Health, Turkey"},{"name":"Computer Engineering Department, Konya Technical University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murat Can","family":"Ganiz","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Marmara University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Halife","family":"Kodaz","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Konya Technical University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ural","family":"Ko\u00e7","sequence":"additional","affiliation":[{"name":"Ministry of Health, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220609","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:17:58Z","timestamp":1653481078000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220609"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220609","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}