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- W2912557990 abstract "Background/Aim: The Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program was launched by the US National Institute of Biomedical Imaging and Bioengineering to develop wearable, sensor-based, integrated health monitoring systems for measuring environmental (e.g., personal air pollution exposure), physiological, and behavioral factors in epidemiological studies of pediatric asthma. Continuous, real-time sensor data pose unique opportunities and challenges compared to typical environmental epidemiology data from cohort or panel studies. To extract meaningful information from heterogeneous sensors, a data integration framework and new statistical approaches that apply over large historical and streaming data are required. While many scientific questions are of interest, a common goal is to identify important triggers of asthma exacerbations. The aim of our study is to develop, apply, and evaluate a statistical analysis framework for predicting asthma exacerbations from sensor data. Methods: Our basic framework consists of a population-level machine learning model (e.g., generalized boosted model) updated to a personalized prediction model on top of a novel data integration and analysis architecture. We evaluate our framework using simulated data and pilot data. Results: Key statistical challenges include effective summarization of sensor data streams (“feature engineering”), the development of independent training and test data sets for model evaluation that appropriately account for temporally and spatially autocorrelated data, feature selection, and interpretation of resultant models. Recent symptoms are typically an important predictor of future exacerbation. Population-level triggers may differ from individual-level triggers. Conclusions: Data arising from sensor-based monitoring systems require modern statistical analysis approaches, but offer an exciting new paradigm for clinical and epidemiological studies." @default.
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- W2912557990 date "2018-02-01" @default.
- W2912557990 modified "2023-10-18" @default.
- W2912557990 title "Methods for Predicting Asthma Exacerbations using Personal Sensor Monitoring Systems" @default.
- W2912557990 doi "https://doi.org/10.1289/isee.2017.2017-436" @default.
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