TY - JOUR
T1 - Predicting Post-Operative Complications with Wearables
T2 - A Case Study with Patients Undergoing Pancreatic Surgery
AU - Zhang, Jingwen
AU - Li, Dingwen
AU - Dai, Ruixuan
AU - Cos, Heidy
AU - Williams, Gregory A.
AU - Raper, Lacey
AU - Hammill, Chet W.
AU - Lu, Chenyang
N1 - Funding Information:
This publication was supported by the Fullgraf Foundation; the Foundation for Barnes Jewish Hospital; and the BJC Health Systems Innovation Lab.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/7
Y1 - 2022/7
N2 - Post-operative complications and hospital readmission are of great concern to surgical patients and health care providers. Wearable devices such as Fitbit wristbands enable long-Term and non-intrusive monitoring of patients outside clinical environments. To build accurate predictive models based on wearable data, however, requires effective feature engineering to extract high-level features from time series data collected by the wearable sensors. This paper presents a pipeline for developing clinical predictive models based on wearable sensors. The core of the pipeline is a multi-level feature engineering framework for extracting high-level features from fine-grained time series data. The framework integrates a set of techniques tailored for noisy and incomplete wearable data collected in real-world clinical studies: (1) singular spectrum analysis for extracting high-level features from daily features over the course of the study; (2) a set of daily features that are resilient to missing data in wearable time series data; (3) a K-Nearest Neighbors (KNN) method for imputing short missing heart rate segments; (4) the integration of patients' clinical characteristics and wearable features. We evaluated the feature engineering approach and machine learning models in a clinical study involving 61 patients undergoing pancreatic surgery. Linear support vector machine (SVM) with integrated feature engineering achieved an AUROC of 0.8802 for predicting post-operative readmission or severe complications, which significantly outperformed the existing rule-based model used in clinical practice and other state-of-The-Art feature engineering approaches.
AB - Post-operative complications and hospital readmission are of great concern to surgical patients and health care providers. Wearable devices such as Fitbit wristbands enable long-Term and non-intrusive monitoring of patients outside clinical environments. To build accurate predictive models based on wearable data, however, requires effective feature engineering to extract high-level features from time series data collected by the wearable sensors. This paper presents a pipeline for developing clinical predictive models based on wearable sensors. The core of the pipeline is a multi-level feature engineering framework for extracting high-level features from fine-grained time series data. The framework integrates a set of techniques tailored for noisy and incomplete wearable data collected in real-world clinical studies: (1) singular spectrum analysis for extracting high-level features from daily features over the course of the study; (2) a set of daily features that are resilient to missing data in wearable time series data; (3) a K-Nearest Neighbors (KNN) method for imputing short missing heart rate segments; (4) the integration of patients' clinical characteristics and wearable features. We evaluated the feature engineering approach and machine learning models in a clinical study involving 61 patients undergoing pancreatic surgery. Linear support vector machine (SVM) with integrated feature engineering achieved an AUROC of 0.8802 for predicting post-operative readmission or severe complications, which significantly outperformed the existing rule-based model used in clinical practice and other state-of-The-Art feature engineering approaches.
KW - Feature Engineering
KW - Machine Learning
KW - Missing Data
KW - Post-Surgical Prediction
KW - Wearable Devices
UR - http://www.scopus.com/inward/record.url?scp=85134228614&partnerID=8YFLogxK
U2 - 10.1145/3534578
DO - 10.1145/3534578
M3 - Article
AN - SCOPUS:85134228614
SN - 2474-9567
VL - 6
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
IS - 2
M1 - 87
ER -