Development and Validation of Machine Learning-Based Models for Predicting Postoperative Depression Risk in Patients With Ovarian Cancer

• Jitong Zhao ^[1] ; Kaige Pei ^[1] ; Junhan Liu ^[1] ; Ce Bian ^[1] ; Chen Ling ^[1]
  1. [1] Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, 610041 Chengdu, Sichuan, China; Key Laboratory of Obstetrics and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, 610041 Chengdu, Sichuan, China
• Localización: Actas españolas de psiquiatría, ISSN 1139-9287, Vol. 54, Nº. 2, 2026, págs. 480-499
• Idioma: inglés
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• Resumen

  • Objective: To develop machine learning-based prediction models for postoperative depression risk in patients with ovarian cancer and to evaluate their predictive performance and clinical application value.

    Results: Among 850 patients, 268 (31.5%) were positive for postoperative depression risk. Feature selection identified 13 predictive variables: age, operation time, length of hospital stay, pain score, white blood cell count, albumin, C-reactive protein, CA125, education level, history of depression/anxiety, postoperative insomnia, fatigue, and opioid analgesic use. Among the five models, random forest demonstrated superior performance with an AUC of 0.776 in the validation set, a Brier score of 0.182, sensitivity of 0.771, and an F1 score of 0.792, along with satisfactory calibration and clinical net benefit. SHAP analysis revealed that pain score, postoperative insomnia, albumin level, and opioid use contributed substantially to model predictions. A nomogram based on logistic regression model was constructed for intuitive individual risk assessment.

    Conclusion: The machine learning-based prediction models for postoperative depression risk in patients with ovarian cancer demonstrated satisfactory discriminative ability and clinical utility, with random forest model showing optimal performance. A clinical nomogram was additionally constructed to enable individualised and visual risk quantification suitable for bedside application. Together, these tools facilitate early identification of high-risk patients and provide evidence for clinical intervention.