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DTSTAMP:20250822T115807Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250617T190000
DTEND;TZID=Europe/Stockholm:20250617T210000
UID:submissions.pasc-conference.org_PASC25_sess151_pos144@linklings.com
SUMMARY:P23 - Harnessing High Performance Computing for Advanced Biomarker
  Discovery from Wearable Device Data: A Pathway to Optimized Therapeutic O
 utcomes
DESCRIPTION:Silvano Coletti (Università degli Studi Guglielmo Marconi, CHE
 LONIA SA) and Francesca Fallucchi (Università degli Studi Guglielmo Marcon
 i)\n\nThe integration of data from smartphones and wearable devices offers
  a groundbreaking opportunity to apply machine learning for advancements i
 n digital health. This project presents a case study demonstrating the app
 lication of advanced machine learning techniques to large-scale, heterogen
 eous datasets, with a focus on identifying clinically relevant biomarkers 
 and enabling personalized therapeutic pathways. The project highlights the
  challenges inherent in managing and analyzing the complexity of physiolog
 ical and environmental data streams, enriched by user annotations such as 
 mood tracking and medication intake. By leveraging high-performance comput
 ing (HPC) infrastructures, the methodology addresses the heterogeneity, vo
 lume, and real-time requirements of these datasets. This poster will provi
 de a detailed examination of how HPC-enabled workflows facilitate the prep
 rocessing, feature extraction, and analysis of multi-modal data. It will a
 lso illustrate the scalability of the approach, offering insights into the
  translation of digital health data into innovative therapeutic interventi
 ons. The discussion will emphasize the computational techniques used, the 
 challenges of HPC adaptation for machine learning, and the clinical releva
 nce of the findings, while focusing on the scalability and reproducibility
  of the methodology.\n\n
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