A stepwise protocol for neural network modeling of persistent postoperative facial pain in chronic rhinosinusitis

• Autorzy: Szaleniec J. , Szaleniec M. , Stręk P.

Identyfikatory

DOI

10.1515/bams-2016-0008

• Języki publikacji: EN

Abstrakty

EN

In the artificial neural network field, no universal algorithm of modeling ensures obtaining the best possible model for a given task. Researchers frequently regard artificial neural networks with suspicion caused by the lack of repeatability of single experiments. We propose a systematic approach that may increase the probability of finding the optimal network architecture. In the experiments, the average effectiveness in groups of networks rather than single networks should be compared. Such an approach facilitates the analysis of the results caused by changes in the network parameters, while the influence of chance effects becomes negligible. As an example of this protocol, we present optimization of a neural network applied for prediction of persistent facial pain in patients operated for chronic rhinosinusitis. In the stepwise approach, the percentage of correct predictions was gradually increased from 54% to 75% for the external validation set.

Słowa kluczowe

EN

artificial neural network; chronic rhinosinusitis; facial pain; modeling; surgery

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2016
• Tom: Vol. 12, no. 2
• Strony: 81--88
• Opis fizyczny: Bibliogr. 30 poz., rys., wykr.

Twórcy

autor

Szaleniec J.

• Department of Otolaryngology, Jagiellonian University Medical College, Krakow, Poland

autor

Szaleniec M.

• Jerzy Haber Institute of Catalysis and Surface Chemistry, PAS, Krakow, Poland

autor

Stręk P.

• Department of Otolaryngology, Jagiellonian University Medical College, Krakow, Poland

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.