<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Jun Chen | Nunez Lab for Computational Mental Health &amp; Cancer Care</title><link>https://example.com/authors/jun-chen/</link><atom:link href="https://example.com/authors/jun-chen/index.xml" rel="self" type="application/rss+xml"/><description>Jun Chen</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Tue, 01 Jun 2021 00:00:00 +0000</lastBuildDate><image><url>https://example.com/media/icon_huf7bff94e036df031df1d4c9565d99cb9_25138_512x512_fill_lanczos_center_3.png</url><title>Jun Chen</title><link>https://example.com/authors/jun-chen/</link></image><item><title>Replication of Machine Learning Methods to Predict Treatment Outcome with Antidepressant Medications in Patients with Major Depressive Disorder from STAR*D and CAN-BIND-1</title><link>https://example.com/publication/antidepressant_ml_replication/</link><pubDate>Tue, 01 Jun 2021 00:00:00 +0000</pubDate><guid>https://example.com/publication/antidepressant_ml_replication/</guid><description>&lt;p>This replication and external validation study demonstrates that machine learning methods can successfully predict antidepressant treatment outcomes, with prediction of remission performing better than prediction of response. These findings support the clinical utility of machine learning approaches for personalized depression treatment planning.&lt;/p></description></item></channel></rss>