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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,7,4]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3534578","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T18:50:18Z","timestamp":1657219818000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting Post-Operative Complications with Wearables"],"prefix":"10.1145","volume":"6","author":[{"given":"Jingwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Washington University in St. Louis, Department of Computer Science and Engineering, St. Louis, Missouri, USA"}]},{"given":"Dingwen","family":"Li","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Computer Science and Engineering, St. Louis, Missouri, USA"}]},{"given":"Ruixuan","family":"Dai","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Computer Science and Engineering, St. Louis, Missouri, USA"}]},{"given":"Heidy","family":"Cos","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Surgery, St. Louis, Missouri, USA"}]},{"given":"Gregory A.","family":"Williams","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Surgery, St. Louis, Missouri, USA"}]},{"given":"Lacey","family":"Raper","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Surgery, St. Louis, Missouri, USA"}]},{"given":"Chet W.","family":"Hammill","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Surgery, St. Louis, Missouri, USA"}]},{"given":"Chenyang","family":"Lu","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, Department of Computer Science and Engineering, St. Louis, Missouri, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Emmanuel PL Dumont, and E Sander Connolly Jr","author":"Appelboom Geoff","year":"2015","unstructured":"Geoff Appelboom, Blake E Taylor, Eliza Bruce, Clare C Bassile, Corinna Malakidis, Annie Yang, Brett Youngerman, Randy D'Amico, Sam Bruce, Olivier Bruy\u00e8re, Jean-Yves Reginster, Emmanuel PL Dumont, and E Sander Connolly Jr. 2015. 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