CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishComorbidities such as hypertension and lipid metabolism are often associated in diseases such as diabetes, and the early prediction of these is of great value when trying to manage progression. This is the start of a project to model multiple comorbidities in diabetes using dynamic Bayesian networks with latent variables in order to stratify patient cohorts. In this paper, we demonstrate some initial results on a dataset where the class imbalance problem poses an issue due to the rare occurrence of different individual comorbidities on a visit-by-visit basis. This is dealt with using a bootstrap technique that has been specifically designed for longitudinal data where the occurrence of the positive class occurs far less than the negative.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Predicting Comorbidities Using Resampling and Dynamic Bayesian Networks with Latent Variables | |
| Autori: | ||
| Data di pubblicazione: | 2017 | |
| Abstract: | Comorbidities such as hypertension and lipid metabolism are often associated in diseases such as diabetes, and the early prediction of these is of great value when trying to manage progression. This is the start of a project to model multiple comorbidities in diabetes using dynamic Bayesian networks with latent variables in order to stratify patient cohorts. In this paper, we demonstrate some initial results on a dataset where the class imbalance problem poses an issue due to the rare occurrence of different individual comorbidities on a visit-by-visit basis. This is dealt with using a bootstrap technique that has been specifically designed for longitudinal data where the occurrence of the positive class occurs far less than the negative. | |
| Handle: | http://hdl.handle.net/11571/1349266 | |
| ISBN: | 978-1-5386-1710-6 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11571/1349266
Copyright © 2021