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https://ruomo.lib.uom.gr/handle/7000/1556
Τίτλος: | Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score |
Συγγραφείς: | Laios, Alexandros De Freitas, Daniel Lucas Dantas Saalmink, Gwendolyn Tan, Yong Sheng Johnson, Racheal Zubayraeva, Albina Munot, Sarika Hutson, Richard Thangavelu, Amudha Broadhead, Tim Nugent, David Kalampokis, Evangelos de Lima, Kassio Michell Gomes Theophilou, Georgios De Jong, Diederick |
Τύπος: | Article |
Θέματα: | FRASCATI::Medical and Health sciences::Clinical medicine |
Λέξεις-Κλειδιά: | machine learning deep learning artificial intelligence surgical cytoreduction epithelial ovarian cancer length of stay graphical user interface |
Ημερομηνία Έκδοσης: | 2022 |
Πηγή: | Current Oncology |
Τόμος: | 29 |
Τεύχος: | 12 |
Πρώτη Σελίδα: | 9088 |
Τελευταία Σελίδα: | 9104 |
Επιτομή: | (1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS. |
URI: | https://doi.org/10.3390/curroncol29120711 https://ruomo.lib.uom.gr/handle/7000/1556 |
ISSN: | 1718-7729 |
Αλλοι Προσδιοριστές: | 10.3390/curroncol29120711 |
Εμφανίζεται στις Συλλογές: | Τμήμα Οργάνωσης & Διοίκησης Επιχειρήσεων |
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Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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curroncol-29-00711.pdf | 1,98 MB | Adobe PDF | Προβολή/Ανοιγμα |
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