<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Keith Ho | Nunez Lab for Computational Mental Health &amp; Cancer Care</title><link>https://example.com/authors/keith-ho/</link><atom:link href="https://example.com/authors/keith-ho/index.xml" rel="self" type="application/rss+xml"/><description>Keith Ho</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Fri, 01 Sep 2023 00:00:00 +0000</lastBuildDate><image><url>https://example.com/media/icon_huf7bff94e036df031df1d4c9565d99cb9_25138_512x512_fill_lanczos_center_3.png</url><title>Keith Ho</title><link>https://example.com/authors/keith-ho/</link></image><item><title>Response Trajectories during Escitalopram Treatment of Patients with Major Depressive Disorder</title><link>https://example.com/publication/escitalopram_response_trajectories/</link><pubDate>Fri, 01 Sep 2023 00:00:00 +0000</pubDate><guid>https://example.com/publication/escitalopram_response_trajectories/</guid><description>&lt;p>This study reveals distinct response trajectories to escitalopram treatment in depression by applying unsupervised machine learning to clinical data. The findings highlight that subjective mood and anhedonia are central to treatment response, while other symptom domains show more variable patterns.&lt;/p></description></item></channel></rss>