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Tytuł: Artificial neural networks for prediction of outcome after surgery for chronic rhinosinusitis

Autor:

Szaleniec, Joanna

Data:

2014

Typ:

Praca doktorska

Abstrakt:

Introduction. Surgical treatment reduces the symptoms of chronic rhinosinusitis (CRS) in most, but not all patients. The choice of optimal treatment modality for each patient can be aided by outcome prediction.Aim. The aim of the study was to build models for individual outcome prediction in patients with CRS.Material and method. The study group comprised of 183 patients who underwent endoscopic sinus surgery for CRS. The preoperative evaluation included anamnesis, laryngological examination, computed tomography and self-assessment of symptoms. The symptoms were reevaluated 3-6 and 12 months after surgery. The results of treatment were predicted using artificial neural networks (self-learning systems inspired by the structure and function of the nervous system).Results. The best obtained models provided correct prognoses of the postoperative symptom reduction in 94% of patients. The individual symptoms were accurately predicted in 70-91% of patients. Effective prognostication did not require data exceeding the routine preoperative investigations.Conclusions. Artificial neural networks are effective tools for the prediction of outcome after surgery for chronic rhinosinusitis and may be adopted in everyday clinical practice.

Miejsce wydania:

Kraków

Stopień studiów:

2 - studia doktoranckie

Dyscyplina:

otorynolaryngologia ; informatyka medyczna

Instytucja nadająca tytuł:

Wydział Lekarski

Promotor:

Stręk, Paweł

Data wydania:

2014

Identyfikator:

oai:dl.cm-uj.krakow.pl:3951

Sygnatura:

ZB-122327

Język:

pol

Prawa dostępu:

nieograniczony

Temat i słowa kluczowe:

chronic rhinosinusitis artificial neural networks modelling

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

26 cze 2023

Data dodania obiektu:

5 mar 2015

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1 275

Liczba wyświetleń treści obiektu w formacie PDF

150

Wszystkie dostępne wersje tego obiektu:

http://dl.cm-uj.krakow.pl:8080/publication/3951

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Wyświetl opis w formacie OAI-PMH:

OAI-PMH

Nazwa wydania Data
ZB-122327 26 cze 2023

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