Pubblicazioni

Autori:

Sala, Pietro; Combi, Carlo; Cuccato, Matteo; Galvani, Andrea; Sabaini, Alberto

Titolo:

A Framework for Mining Evolution Rules and Its Application to the Clinical Domain

Anno:

2015

Tipologia prodotto:

Contributo in atti di convegno

Tipologia ANVUR:

Contributo in Atti di convegno

Lingua:

Inglese

Formato:

Elettronico

Titolo del Convegno:

2015 International Conference on Healthcare Informatics (ICHI)

Luogo:

Dallas, TX, USA

Periodo:

Oct. 21, 2015 to Oct. 23, 2015

Casa editrice:

IEEE Comp. Society Press

ISBN:

978-1-4673-9548-9

Intervallo pagine:

293-302

Parole chiave:

Temporal data mining, Approximate temporal functional dependencies, pharmacovigilance, psychiatry

Breve descrizione dei contenuti:

Database constraints, such as "patients with the same symptoms get the same therapies", may be modeled by means of functional dependencies (FD). They have been extended to represent temporal constraints such as "patients with the same symptoms and the same administered therapies, receive in the next period the same therapies". These constraints are called temporal functional dependencies (TFD). Another extension for FDs allows one to represent approximate functional dependencies (AFDs), as "patients with the same symptoms generally get the same therapy". It enables data to deviate from the defined constraints according to a user-defined percentage. By merging the concepts of temporal functional dependency and of approximate functional dependency, we obtain the concept of approximate temporal functional dependency (ATFD). Mining ATFDs from large databases may be an hard job from the computational point of view. Moreover, convenient and meaningful representations of mined results are needed for conveying knowledge to domain experts. In this paper, we propose a framework for mining complex ATFDs and a way to represent data satisfying such ATFDs, which is informative as well as human-readable. Within the framework, we designed and applied sound and advanced model-checking techniques. For proving the feasibility of our proposal, we used real world databases from two medical domains (namely, psychiatry and pharmacovigilance) and tested the running prototype we developed on such databases.

Id prodotto:

89609

Handle IRIS:

11562/932639

ultima modifica:

15 novembre 2022

Citazione bibliografica:

Sala, Pietro; Combi, Carlo; Cuccato, Matteo; Galvani, Andrea; Sabaini, Alberto, A Framework for Mining Evolution Rules and Its Application to the Clinical Domain  in 2015 International Conference on Healthcare Informatics, {ICHI} 2015 , IEEE Comp. Society Press , Atti di "2015 International Conference on Healthcare Informatics (ICHI)" , Dallas, TX, USA , Oct. 21, 2015 to Oct. 23, 2015 , 2015 , pp. 293-302

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