Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables

• Main Authors: Bouwman, R.A (Author), Cox, L.G.E (Author), Mestrom, E.H.J (Author), van den Eijnden, M.A.C (Author), van der Stam, J.A (Author), van Riel, N.A.W (Author), Verhaegh, W.F.J (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: clinical prediction; Clinical prediction; 'current; Decision making; Decision support systems; Decisions makings; hospital discharge; Hospital discharge; Hospitals; machine learning; Machine learning; Machine-learning; monitoring; oncology; Oncology; physical activity; Physical activity; post-operative recovery; Post-operative recovery; Recovery; Recovery profiles; Regression analysis; Regression modelling; Vital sign; vital signs; wearable sensors; Wearable sensors
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 14248220 (ISSN)
• DOI: 10.3390/s23094455

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool. © 2023 by the authors.