<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>healthcare on Wonderland of Deisy Gysi</title><link>https://deisygysi.github.io/tags/healthcare/</link><description>Recent content in healthcare on Wonderland of Deisy Gysi</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://deisygysi.github.io/tags/healthcare/index.xml" rel="self" type="application/rss+xml"/><item><title>AI for Healthcare</title><link>https://deisygysi.github.io/projects/ai-healthcare/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://deisygysi.github.io/projects/ai-healthcare/</guid><description>Summary Artificial intelligence can help connect complex biomedical data to useful predictions, but high benchmark performance does not guarantee that a model will generalize to new molecules, populations or clinical settings. Our research examines both the opportunities and the limits of machine learning in healthcare.
We combine AI with network-based representations to improve prediction, interpretability and evaluation. The emphasis is on methods that reveal the evidence behind a prediction, avoid shortcuts hidden in biomedical datasets and remain useful beyond well-annotated examples.</description></item></channel></rss>