{"id":9779,"date":"2024-10-10T13:27:49","date_gmt":"2024-10-10T10:27:49","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=9779"},"modified":"2025-11-03T13:49:23","modified_gmt":"2025-11-03T11:49:23","slug":"2024-testing-ai-and-ml-tools-for-structuring-unstructured-medical-texts","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2024-testing-ai-and-ml-tools-for-structuring-unstructured-medical-texts\/","title":{"rendered":"2024: Testing AI and ML tools for structuring unstructured medical texts"},"content":{"rendered":"

The <\/span>project<\/span> tackled<\/span> the<\/span> challenge<\/span> of <\/span>processing<\/span> unstructured<\/span> Estonian <\/span>clinical<\/span> texts<\/span>, <\/span>which<\/span> are <\/span>resource-intensive<\/span> and <\/span>lack<\/span> machine-readability<\/span>, hindering <\/span>healthcare<\/span> efficiency<\/span>. The aim <\/span>was<\/span> to<\/span> test <\/span>an<\/span> AI tool <\/span>that<\/span> automatically<\/span> structures<\/span> medical<\/span> free<\/span> text<\/span> and <\/span>maps<\/span> it<\/span> to<\/span> SNOMED CT <\/span>codes<\/span> to<\/span> reduce<\/span> administrative<\/span> burden<\/span>. Natural <\/span>Language<\/span> Processing<\/span> (NLP) <\/span>technologies<\/span>, <\/span>primarily<\/span> Microsoft <\/span>Azure<\/span> Text<\/span> Analytics<\/span> for<\/span> Health, <\/span>were<\/span> validated<\/span>. A <\/span>web-based<\/span> prototype<\/span> was<\/span> tested<\/span> to<\/span> perform<\/span> named<\/span> entity<\/span> recognition<\/span>, <\/span>relationship<\/span> extraction<\/span>, and <\/span>generate<\/span> FHIR-<\/span>compliant<\/span> outputs<\/span>. The <\/span>prototype<\/span> was<\/span> tested<\/span> on <\/span>general<\/span> practitioner<\/span> notes<\/span> and <\/span>radiology<\/span> reports<\/span>, <\/span>with<\/span> three<\/span> doctors<\/span> annotating<\/span> the<\/span> texts<\/span>. <\/span>It<\/span> successfully<\/span> identified<\/span> clinical<\/span> terms<\/span> and <\/span>mapped<\/span> them<\/span> to<\/span> SNOMED CT, <\/span>though<\/span> challenges<\/span> persisted<\/span> with<\/span> Estonian <\/span>morphological<\/span> variations<\/span>. The <\/span>solution<\/span> reduced<\/span> manual<\/span> processing<\/span> and <\/span>improved<\/span> data<\/span> quality<\/span>. <\/span>Future<\/span> plans<\/span> include<\/span> model<\/span> fine-tuning<\/span> and <\/span>integration<\/span> with<\/span> Estonia\u2019s<\/span> health<\/span> information<\/span> system<\/span>. The <\/span>project<\/span> supports<\/span> time<\/span> savings<\/span> and <\/span>enhanced<\/span> diagnostic<\/span> accuracy<\/span> in <\/span>healthcare<\/span>.<\/span><\/p>\n

Github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

The project tackled the challenge of processing unstructured Estonian clinical texts, which are resource-intensive and lack machine-readability, hindering healthcare efficiency. The aim was to test an AI tool that automatically […]<\/p>\n","protected":false},"featured_media":10625,"template":"","year_tax":[86],"company_tax":[208],"status_tax":[92],"university_tax":[90],"sector":[],"project_cat":[65],"class_list":["post-9779","project","type-project","status-publish","has-post-thumbnail","hentry","year_tax-2024-en","company_tax-smartehr-technologies-ou-en","status_tax-completed-projects","university_tax-taltech-en","project_cat-completed-projects"],"acf":[],"yoast_head":"\nAIRE - 2024: Testing AI and ML tools for structuring unstructured medical texts<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aire-edih.eu\/en\/project\/2024-testing-ai-and-ml-tools-for-structuring-unstructured-medical-texts\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AIRE - 2024: Testing AI and ML tools for structuring unstructured medical texts\" \/>\n<meta property=\"og:description\" content=\"The project tackled the challenge of processing unstructured Estonian clinical texts, which are resource-intensive and lack machine-readability, hindering healthcare efficiency. 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