<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Emma Morton | Nunez Lab for Computational Mental Health &amp; Cancer Care</title><link>https://example.com/authors/emma-morton/</link><atom:link href="https://example.com/authors/emma-morton/index.xml" rel="self" type="application/rss+xml"/><description>Emma Morton</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Wed, 01 Nov 2023 00:00:00 +0000</lastBuildDate><image><url>https://example.com/media/icon_huf7bff94e036df031df1d4c9565d99cb9_25138_512x512_fill_lanczos_center_3.png</url><title>Emma Morton</title><link>https://example.com/authors/emma-morton/</link></image><item><title>Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report</title><link>https://example.com/publication/quality_life_antidepressant_ml/</link><pubDate>Wed, 01 Nov 2023 00:00:00 +0000</pubDate><guid>https://example.com/publication/quality_life_antidepressant_ml/</guid><description>&lt;p>This study demonstrates that machine learning can predict quality of life improvements in depression treatment, supporting the use of early clinical indicators to identify patients likely to benefit from antidepressant therapy in terms of functional outcomes.&lt;/p></description></item></channel></rss>