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Sumi-supervised learning algorithms in mHealth domain

Grant number: 23/05171-0
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): May 01, 2023
Effective date (End): January 31, 2028
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Diego Furtado Silva
Grantee:Yuri Gabriel Aragão da Silva
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:22/03176-1 - Machine learning for time series obtained in mHealth applications, AP.PNGP.PI


Monitoring physiological signs, vital signs, and other parameters that can be collected over time is essential in several tasks in Health, such as estimating heart rate and identifying abnormal heartbeats. However, obtaining annotated data, especially in the Health domain, can be costly. However, in some cases, obtaining labels for part of the data is possible. This scenario configures semi-supervised learning, a category of Machine Learning algorithms capable of using the information in a few annotated examples to obtain potentially better models than those induced without supervision or only with the few labeled data present in the set. Semi-supervised learning can also rely on other assumptions about data annotations, as in one-class and positive and unlabeled learning. This work will explore the potential of Machine Learning for time series in mHealth applications with the different annotation assumptions described. Therefore, this activity aims to adapt or create algorithms and neural architectures from semi-supervised learning for this application domain. (AU)

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