A novel sparse linear mixed model for multi-source mixed-frequency data fusion in telemedicine

Abstract

Digital health and telemonitoring have resulted in a wealth of information to be collected to monitor, manage, and improve human health. The multi-source mixed-frequency health data overwhelm the modeling capacity of existing statistical and machine learning models, due to many challenging properties. Although predictive analytics for big health data plays an important role in telemonitoring, there is a lack of rigorous prediction model that can automatically predicts patients’ health conditions, e.g., Disease Severity Indicators (DSIs), from multi-source mixed-frequency data. Sleep disorder is a prevalent cardiac syndrome that is characterized by abnormal respiratory patterns during sleep. Although wearable devices are available to administrate sleep studies at home, the manual scoring process to generate the DSI remains a bottleneck in automated monitoring and diagnosis of sleep disorder. To address the multi-fold challenges for precise prediction of the DSI from high-dimensional multi-source mixed-frequency data in sleep disorder, we propose a sparse linear mixed model that combines the modified Cholesky decomposition with group lasso penalties to enable joint group selection of fixed effects and random effects. A novel Expectation Maximization (EM) algorithm integrated with an efficient Majorization Maximization (MM) algorithm is developed for model estimation of the proposed sparse linear mixed model with group variable selection. The proposed method was applied to the SHHS data for telemonitoring and diagnosis of sleep disorder and found that a few significant feature groups that are consistent with prior medical studies on sleep disorder. The proposed method also outperformed a few benchmark methods with the highest prediction accuracy.

Keywords:

• Linear mixed model
• group lasso
• multi-source mixed-frequency data
• telemonitoring

Disclosure statement

The authors declare no conflict of interest.

Consent and approval

This study has been exempt from the requirement for approval by an institutional review board, because it is a secondary analysis of de-identified data with an approved data use agreement.

Role of the funder

The opinions expressed in this document do not reflect the official position of NIH, U.S. Department of Health and Human Services, SUNY, or IBM.

Additional information

Funding

This work was partially supported by the National Institutes of Health (NIH) under Grants R03HD108477 and R21HL161765; State University of New York (SUNY); and IBM. The authors would like to thank the investigators and researchers of the SHHS for collecting, preprocessing, and sharing the data. The SHHS was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University).