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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-06102021-173453


Tipo di tesi
Tesi di laurea magistrale
Autore
ESPOSITO, CHRISTIAN
URN
etd-06102021-173453
Titolo
Preventing catastrophes through PPG: feature extraction and critical event prediction from an ICU patient cohort
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Nanni, Mirco
Parole chiave
  • feature engineering
  • healthcare
  • intensive care unit
  • machine learning
  • time series
  • waveform data
Data inizio appello
25/06/2021
Consultabilità
Completa
Riassunto
Machine learning is steadily changing healthcare around the world, from smartwatches monitoring health behavior to NLP applications for automated diagnosis. Within this wide field of research, one promising subfield is represented by bedside applications in hospital’s intensive care units (ICUs). ICUs monitor closely the physiological state of a critical patient and some hospitals have started collecting data and researching for a way to assist the complex task of caring for a critical patient. One of these hospitals is the Sick Children Hospital of Toronto, which is collaborating to this thesis project. 
Together with Sick Children’s doctors we selected a cohort of ICU patients derived from MIT’s MIMIC-III database, singled out the PPG (photoplethysmogram) waveform signal and performed feature engineering in order to apply prediction models for critical events, such as circulatory failure. Other than trying to predict critical events the thesis aims to understand whether feature engineering in the medical context is still useful, given the automated feature extraction capabilities of modern ML techniques.
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