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Can I read Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery on EtoBox?
Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery by Jingwen Zhang; Dingwen Li; Ruixuan Dai; Heidy Cos; Gregory A. Williams; Lacey Raper; Chet W. Hammill; Chenyang Lu is a Computer Science article available to read on EtoBox.
What is Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery about?
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 feat
Who reads Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jingwen Zhang; Dingwen Li; Ruixuan Dai; Heidy Cos; Gregory A. Williams; Lacey Raper; Chet W. Hammill; Chenyang Lu
- Publisher
- ACM
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)