- Informed machine learning for epidemics: from data analysis to time-series forecasting
- Baldo, Federico <1994>
Subject
- ING-INF/05 Sistemi di elaborazione delle informazioni
Description
- This research dives deep into the application of Machine Learning (ML) in epidemiology, aiming to craft innovative strategies to address challenges presented by epidemic phenomena. Recognizing the dual nature of epidemics — as complex physical occurrences and as catalysts of pressure on healthcare systems — we focus on two main areas: data analysis for patient condition assessment and time-series forecasting for epidemiological trend prediction. In the realm of data analysis, we introduced a tree-based ML pipeline apt at evaluating the severity of infections using patient records. On the forecasting front, we addressed the challenges posed by the scarcity of data and the intricate nature of epidemics, developing methodologies for predicting epidemiological trends. In this setting, we exploit domain knowledge to guide the learning process, ensuring accurate and reliable predictions even when confronted with incomplete and noisy data.
Date
- 2024-03-21
Type
- Doctoral Thesis
- PeerReviewed
Format
- application/pdf
Identifier
urn:nbn:it:unibo-30281
Baldo, Federico (2024) Informed machine learning for epidemics: from data analysis to time-series forecasting, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Computer science and engineering