Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records
Enrico Manzini
PhD researcher at B2SLab, UPC. Research focuses on deep learning for Electronic Health Records, modelling chronic diseases such as Type 2 Diabetes and COPD using transformers and RNNs.
Keywords
deep learning, EHR, Electronic Health Records, transformer, Type 2 Diabetes, COPD, UPC, machine learning
Bio
Welcome to my page! My name is Enrico Manzini. I earned my Bachelor’s degree in Information Engineering from the University of Padua (UNIPD). I then participated in the T.I.M.E. Double Degree program, through which I obtained a Master’s degree in Biomedical Engineering (UNIPD, Padova, Italy) and a Master’s degree in Automatic Control & Robotics (UPC, Barcelona, Spain).
Currently, I’m in the final stages of my PhD at B2SLab (legends say it actually does end someday!) under the supervision of Alex Perera. My research focuses on deep learning (DL) for Electronic Health Records (EHRs), modeling chronic diseases like Type 2 Diabetes and COPD, and predicting clinically relevant outcomes using various DL architectures (e.g., transformers, RNNs, …).
Teaching
Meanwhile, I contribute to teaching in the following courses:
- Deep Learning Methods for Biomedicine, a deep learning course at the Master’s degree in Biomedical Engineering at Universitat de Barcelona (UB).
- Scientific Python for Data Analysis, Python course for Data Analysis for the Aurora consortium of research intensive universities.
Publications
A possibly incomplete and malformed list of publications is included below:
20222 publications
Mapping layperson medical terminology into the Human Phenotype Ontology using neural machine translation models