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https://ruomo.lib.uom.gr/handle/7000/1680| Title: | RoBERTa-Assisted Outcome Prediction in Ovarian Cancer Cytoreductive Surgery Using Operative Notes |
| Authors: | Laios, Alexandros Kalampokis, Evangelos Mamalis, Marios Evangelos Tarabanis, Constantine Nugent, David Thangavelu, Amudha Theophilou, Georgios De Jong, Diederick |
| Type: | Article |
| Subjects: | FRASCATI::Medical and Health sciences FRASCATI::Engineering and technology |
| Keywords: | epithelial ovarian cancer completecy to reduction operative notes natural language processing machine learning transfer learning RoBERTa explainable artificial intelligence |
| Issue Date: | 2023 |
| Source: | Cancer Control |
| Volume: | 30 |
| Abstract: | Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the prediction of residual disease in women with advanced epithelial ovarian cancer (EOC) following cytoreductive surgery. |
| URI: | https://doi.org/10.1177/10732748231209892 https://ruomo.lib.uom.gr/handle/7000/1680 |
| ISSN: | 1073-2748 1526-2359 |
| Other Identifiers: | 10.1177/10732748231209892 |
| Appears in Collections: | Department of Business Administration |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| laios-et-al-2023-roberta-assisted-outcome-prediction-in-ovarian-cancer-cytoreductive-surgery-using-operative-notes.pdf | 1,42 MB | Adobe PDF |  View/Open |
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