Predicting Surgical Outcomes Using Wearables

Abstract

Accurate prediction of readmission and severe complication risks during the pre-operative stage is crucial for clinical decision-making. In addition to traditional static clinical data, the activity and physiological data collected by commercial wearable devices such as Fitbit wristbands provide valuable complementary information. However, data from wearables are often incomplete and noisy, presenting challenges in extracting clinically meaningful features from fine-grained time series data. In this paper, we propose a pipeline to develop predictive models using pre-operative wearable data to predict surgical outcomes in patients undergoing pancreatic surgeries. The core of this pipeline is a multi-level feature engineering framework designed to extract high-level features from wearable data, which are then combined with static clinical characteristics to form the input for the predictive model. In a clinical study involving 61 patients, our approach improved the Area Under the Receiver Operating Characteristic (AUROC) by 44% compared to the existing risk calculator employed in clinical practice.