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Fast Artifical Neural Networks Kütüphanesi hakkında


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13 Mart 2015 Cuma

Integration of Neonatal Mortality Prediction Models into a Clinical Decision Support System

Integration of Neonatal Mortality Prediction Models into a Clinical Decision Support System
Hasmik Martirosyan, B.Sc.

A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs
in partial fulfillment of the requirements for the degree of
Master of Applied Science
Biomedical Engineering
Ottawa - Carleton Institute for Biomedical Engineering (OCIBME)
Carleton University
Ottawa, Ontario © 2015


Yenidoğan Ölüm Tahmin Modellerinin Klinik Karar Destek Sistemine  Entegrasyonu.

This thesis describes the development of neonatal mortality risk estimation models using Artificial Neural Networks (ANNs), the integration of these models into the Physician-Parent Decision Support (PPADS) tool, and the pilot study to test the PPADS tool.
A set of data mining programs were created to automate the data preparation, the development of ANN models and the selection of models that satisfy the usefulness criteria specified by our clinician partners. These programs were used to classify neonatal mortality data (6% mortality rate) with the average sensitivity and specificity of 81% and 98% respectively.
The mortality models were integrated with the PPADS tool to provide predictions about the risk of mortality for neonates admitted to the Neonatal Intensive Care Unit (NICU).
The observational and survey study conducted with parents whose infant did not graduate (died) from the NICU gave encouraging results regarding the usefulness of the PPADS tool.

Fast Artificial Neural Network library
In order to create ANN models, and make predictions in real time environment, we have explored several open sources libraries. We found the Fast Artificial Neural Network (FANN) library to be suitable to our work for the following reasons: firstly, the library implements feed-forward networks, which our research group has identified to be well performing machine learning methods for our medical datasets; secondly, the fast performance is one of the main features of the library, which is important in processing and analyzing real-time data; lastly, the library is implemented in C language which makes the FANN library based applications compatible with many software environments and portable to many different computer architectures or platforms (Nissen, 2007), (FANN, 2014).