Predicting Post-Operative Complications with Wearables

Jingwen Zhang, Dingwen Li, Ruixuan Dai, Heidy Cos, Gregory A. Williams, Lacey Raper, Chet W. Hammill, Chenyang Lu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number87
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume6
Issue number2
DOIs
StatePublished - Jul 2022

Keywords

  • Feature Engineering
  • Machine Learning
  • Missing Data
  • Post-Surgical Prediction
  • Wearable Devices

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