A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images.

Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.

Switching-State Dynamical Modeling of Daily Behavioral Data.

Emerging wearable and environmental sensor technologies provide health professionals with unprecedented capacity to continuously collect human behavioral data for health monitoring and management. This enables new solutions to mitigate globally emerging health problems such as obesity. With such outburst of dynamic sensor data, it is critical that appropriate mathematical models and computational methods are developed to translate the collected data into accurate characterization of the underlying health dynamics, enabling more reliable personalized monitoring, prediction, and intervention of health status changes. In addition to addressing common analytic challenges in analyzing sensor behavioral data, such as missing values and outliers, we focus on modeling heterogeneous dynamics to better capture health status changes under different conditions, which may lead to more effective state-dependent intervention strategies. We implement switching-state dynamic system models with different complexity levels on real-world daily behavioral data. Evaluation experiments of these models are conducted to demonstrate the importance of modeling the dynamic heterogeneity, as well as simultaneously conducting missing value imputation and outlier detection in achieving interpretable health dynamic models with better prediction of health status changes.