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<li>Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data <a href="https://www.frontiersin.org/articles/10.3389/fpsyt.2021.625247/full">link</a></li>
<li>Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study <a href="https://mhealth.jmir.org/2021/7/e26540">link</a></li>
<li>Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study <a href="https://cancer.jmir.org/2021/2/e27975/">link</a></li>
<li>An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data <a href="https://edas.info/showManuscript.php?m=1570708269&amp;random=750318666&amp;type=final&amp;ext=pdf&amp;title=PDF+file">link</a></li>
<li>An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data <a href="https://ieeexplore.ieee.org/abstract/document/9483755/">link</a></li>
<li>Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity <a href="https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract">link</a></li>
</ul>
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