TY - GEN
T1 - Health Label and Behavioral Feature Prediction Using Bayesian Hierarchical Vector Autoregression Models
AU - Lyon, Ethan N.
AU - Victor, Luis H.
AU - Sano, Akane
N1 - Funding Information:
This work is supported by NSF #2047296 and #1840167.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The rising availability and accessibility of data from wearable devices and ubiquitous sensors allow the leveraging of computational methods to address human health and behavioral challenges. In particular, recent works have created time series, interpretable, and generalizable models for predicting patient healthcare outcomes from multidimensional data including expensive self-reported patient data, clinical data, and data from mobile and wearable devices. In this work, we used a Bayesian Hierarchical Vector Autoregression (BHVAR) model to predict behavioral and self-reported health outcomes on college student participants from passively collected data from their smartphones, wearable devices, and environment, as well as their self-reports. We also evaluated how the model performed being trained on 3, 7, 11, and 13 different features including some actionable and modifiable behavioral features. Then, we showed the value of augmenting self-reported datasets with many different types of data by demonstrating that additional inferences can be made with no significant toll on accuracy in comparison to using only self-reported features. Our models proved to be robust despite the greatly increased variable count as the reduced mean squared error (RMSE) of BHVAR over the patient-specific, maximum likelihood estimate (MLE) model was 10.5%, 14.9%, 26.6%, 39.6% in the 3, 7, 11, and 13 variable models respectively. We also obtained patient-level insights from clustering analysis of patient-level coefficients.
AB - The rising availability and accessibility of data from wearable devices and ubiquitous sensors allow the leveraging of computational methods to address human health and behavioral challenges. In particular, recent works have created time series, interpretable, and generalizable models for predicting patient healthcare outcomes from multidimensional data including expensive self-reported patient data, clinical data, and data from mobile and wearable devices. In this work, we used a Bayesian Hierarchical Vector Autoregression (BHVAR) model to predict behavioral and self-reported health outcomes on college student participants from passively collected data from their smartphones, wearable devices, and environment, as well as their self-reports. We also evaluated how the model performed being trained on 3, 7, 11, and 13 different features including some actionable and modifiable behavioral features. Then, we showed the value of augmenting self-reported datasets with many different types of data by demonstrating that additional inferences can be made with no significant toll on accuracy in comparison to using only self-reported features. Our models proved to be robust despite the greatly increased variable count as the reduced mean squared error (RMSE) of BHVAR over the patient-specific, maximum likelihood estimate (MLE) model was 10.5%, 14.9%, 26.6%, 39.6% in the 3, 7, 11, and 13 variable models respectively. We also obtained patient-level insights from clustering analysis of patient-level coefficients.
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U2 - 10.1109/EMBC46164.2021.9630732
DO - 10.1109/EMBC46164.2021.9630732
M3 - Conference contribution
C2 - 34891744
AN - SCOPUS:85122529897
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2290
EP - 2293
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -