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

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

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...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 9; p. 4455
Main Authors: van den Eijnden, Meike A C, van der Stam, Jonna A, Bouwman, R Arthur, Mestrom, Eveline H J, Verhaegh, Wim F J, van Riel, Natal A W, Cox, Lieke G E
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 02-05-2023
MDPI
Subjects:
Algorithms
Cancer surgery
Circadian rhythms
Exercise
Heart beat
Heart rate
hospital discharge
Humans
Machine Learning
Medical research
Medicine, Experimental
Mortality
Neoplasms
oncology
Patients
Perioperative Care
physical activity
Physical therapy
post-operative recovery
Recovery (Medical)
Respiration
Sensors
Therapeutics, Physiological
Variables
Vital signs
Wearable Electronic Devices
wearable sensors
Netherlands
vital signs
hospital discharge
wearable sensors
clinical prediction
machine learning
monitoring
post-operative recovery
oncology
physical activity
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23094455