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Natural Language Processing (NLP), particularly Named Entity Recognition (NER), has been instrumental in extracting structured information from EHR data. However, existing literature primarly focuses on extracting handcrafted clinical features through NLP and NER methods without delving into their learned representations. In this work, we explore the untapped potential of these representations by considering their contextual richness and entity-specific information. Our proposed methodology extracts representations generated by a transformer-based NER model on EHRs data, combines them using a hierarchical attention mechanism, and employs the obtained enriched representation as input for a clinical prediction model. Specifically, this study addresses Overall Survival (OS) in Non-Small Cell Lung Cancer (NSCLC) using unstructured EHRs data collected from an Italian clinical centre encompassing 838 records from 231 lung cancer patients. Whilst our study is applied on EHRs written in Italian, it serves as use case to prove the effectiveness of extracting and employing high level textual representations that capture relevant information as named entities. Our methodology is interpretable because the hierarchical attention mechanism highlights the information in EHRs that the model considers the most crucial during the decision-making process. We validated this interpretability by measuring the agreement of domain experts on the importance assigned by the hierarchical attention mechanism to EHRs information through a questionnaire. Results demonstrate the effectiveness of our method, showcasing statistically significant improvements over traditional manually extracted clinical features.<\/jats:p>","DOI":"10.1186\/s12911-025-02998-6","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T10:04:09Z","timestamp":1744970649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hierarchical embedding attention for overall survival prediction in lung cancer from unstructured EHRs"],"prefix":"10.1186","volume":"25","author":[{"given":"Domenico","family":"Paolo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Greco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Cortellini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Ramella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Soda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Bria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Sicilia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"2998_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-78503-5","volume-title":"Clinical Text Mining: secondary Use of Electronic Patient Records","author":"H Dalianis","year":"2018","unstructured":"Dalianis H. 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Ethical approval was obtained from Fondazione Policlinico Universitario Campus Bio-Medico Ethical Committee: the first approved on 30 October 30 2012 and registered at ClinicalTrials.gov on 12 July 2018 with Identifier NCT03583723; the second approved on 16 April 2019 with Identifier 16\/19 OSS. The authors confirm that all ongoing and related trials for this intervention are registered and written informed consent was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"During the preparation of this work the authors used ChatGPT in order to improve language and readability. After using this tool\/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of generative AI and AI-assisted technologies in the writing process"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"169"}}