|Title / Titel||Dealing with data sparseness of combination therapies in the SHCS dataset|
|Abstract (PDF, 14 KB)|
|Summary / Zusammenfassung||Background:
Finding an optimal anti-HIV treatment for a given patient is very challenging due to the large amount of information that needs to be considered. Applying advanced statistical learning methodologies on the large amount of available clinical data offers a framework for an automated approach for HIV therapy screening that can assist this process.
The main goal of this study is to analyze the treatment change episodes (TCEs) available in the SHSC database using modern statistical learning methods and thereby enhance the clinical management of HIV patients.
First, machine learning models for HIV therapy screening that deal with the sparse, uneven therapy representation are applied on the relevant samples from the SHCS dataset (extracted using the quality control filters derived in a previous SHCS project #629). Next, we model viral evolutionary escape from the selective pressure of each antiretroviral combination therapy used in clinical practice.
|Project leadership and contacts /
Projektleitung und Kontakte
|Other links to external web pages||http://www.shcs.ch|
|Funding source(s) /
|SNF (Personen- und Projektförderung)
SNF, Swiss HIV Cohort Study
|In collaboration with /
In Zusammenarbeit mit
|Duration of Project / Projektdauer||Jan 2015 to Dec 2017|