Pubblicazioni

Autori:

Amico, Beatrice; Combi, Carlo

Titolo:

A 3-Window Framework for the Discovery and Interpretation of Predictive Temporal Functional Dependencies

Anno:

2022

Tipologia prodotto:

Contributo in volume (Capitolo o Saggio)

Tipologia ANVUR:

Contributo in volume (Capitolo o Saggio)

Lingua:

Inglese

Formato:

Elettronico

Titolo libro:

Artificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14-17, 2022, Proceedings

Casa editrice:

Springer Nature

ISBN:

978-3-031-09341-8

Intervallo pagine:

299-309

Parole chiave:

Temporal data mining, Predictive patterns, Functional dependencies, Temporal windows

Breve descrizione dei contenuti:

Clinical databases collect large volume of data. Relationships and patterns within these data could provide new medical knowledge. Temporal data mining has as major scope the discovery of potential hidden knowledge from large amounts of data, offering the possibility to identify different features less visible or hidden to common analysis techniques. In this work, we show how temporal data mining, precisely mining of functional dependencies, can be fruitfully exploited to improve clinical prediction. To develop an early prediction model, a window-based data aggregation approach could be a good starting point, therefore we introduce a new temporal framework based on three temporal windows designed to extract predictive information. In particular, we propose a methodology for deriving a new kind of predictive temporal patterns. We exploit the predictive aspect of the approximate temporal functional dependencies, formally introducing the concept of Predictive Functional Dependency (PFD), a new type of approximate temporal functional dependency. We discuss some first results we obtained by pre-processing and mining ICU data from the MIMIC III database, focusing on functional dependencies predictive of Acute kidney injury (AKI).

Pagina Web:

https://link.springer.com/chapter/10.1007/978-3-031-09342-5_29

Id prodotto:

129393

Handle IRIS:

11562/1073573

ultima modifica:

15 aprile 2023

Citazione bibliografica:

Amico, Beatrice; Combi, Carlo, A 3-Window Framework for the Discovery and Interpretation of Predictive Temporal Functional Dependencies Artificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14-17, 2022, Proceedings , Springer Nature , 2022 , pp. 299-309

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