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Title: 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
Authors: 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
Type: Article
Subjects: FRASCATI::Medical and Health sciences::Clinical medicine
Keywords: machine learning
deep learning
artificial intelligence
surgical cytoreduction
epithelial ovarian cancer
length of stay
graphical user interface
Issue Date: 2022
Source: Current Oncology
Volume: 29
Issue: 12
First Page: 9088
Last Page: 9104
Abstract: (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.
ISSN: 1718-7729
Other Identifiers: 10.3390/curroncol29120711
Appears in Collections:Department of Business Administration

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