A novel sparse linear mixed model for multi-source mixed-frequency data fusion in telemedicine
Year: 2023
Type: article
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.
Authors: Wesam Alramadeen, Yu Ding, Carlos Costa, Bing Si
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: New York University, Boston University, University of Minnesota, State University of New York, University of Washington, Johns Hopkins University, University of Arizona, Case Western Reserve University, York University, National Institutes of Health, University of California, Davis, Johns Hopkins University Press, National Heart, Lung, and Blood Institute, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G101872767, G1143380228, G1183342534, G145926711, G1466802490, G1501427085, G1539514234, G1616783892, G1635395577, G1658652321, Sleep Heart Health Study, G2004107639, G2221547554, CARDIOVASCULAR CONSEQUENCES OF SLEEP APNEA, The Sleep Heart Health Study (SHHS), G2864620425, G2979886203, G3028172619, The Sleep Heart Health Study, G3337000151, G3405042059, G3413581171, G3579933184, Sleep and Cardiometabolic Health in United States Hispanic/Latino Late Adolescents/Young Adults, SLEEP HEART HEALTH STUDY, G4004599211, G4236403604, G4660834155, Data Coordinating Center for Sleep Heart Health, G4750466064, G4831393589, Sleep Heart Health Study, G497331810, G5012393887, G517033781, G528789731, G5307255347, Towards Precise Phenotype Discovery of Obstructive Sleep Apnea with a Data-Inclusive Multi-Study Analysis Using the National Sleep Research Resource (NSRR), Sleep Heart Health Study (SHHS), G6077142251, G6661059585, G6726033632, G67285121, G673138302, The Sleep Heart Health Study, G7049934025, G7425785020, G7477163181, G7517817407, G7828690878, G8282785547, G8296645940, G8506020310, G951269807
OpenAlex ID: W4366279575