Artificial neural networks in neurorehabilitation: A scoping review

AbstractBACKGROUNDAdvances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the use of ANNs in neurorehabilitation is limited. OBJECTIVE: To explore the current use of ANNs in neurorehabilitation. METHODS: PubMed, CINAHL, and Web of Science were used for literature search. Studies in the scoping review (1) utilized ANNs, (2) examined populations with neurological conditions, and (3) focused rehabilitation outcomes. The initial search identified 1,136 articles. A total of 19 articles were included. RESULTS: ANNs were used for prediction of functional outcomes and mortality (n = 11) and classification of motor symptoms and cognitive status (n = 8). Most ANN-based models outperformed regression or other machine learning models (n = 11) and showed accurate performance (n = 6; no comparison with other models) in predicting clinical outcomes and accurately classifying different neurological impairments.CONCLUSIONSThis scoping review provides encouraging evidence to use ANNs for clinical decision-making of complex datasets in neurorehabilitation. However, more research is needed to establish the clinical utility of ANNs in diagnosing, monitoring, and rehabilitation of individuals with neurological conditions.

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Use of artificial neural networks for clinical decision-making (Maldescensus testis)

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks ◽

10.1109/ijcnn.1992.227176 ◽

2003 ◽

Cited By ~ 1

Author(s):

G.M. Papadourakis ◽

E. Gaga ◽

G. Vareltzis ◽

G. Bebis

Keyword(s):

Neural Networks ◽

Decision Making ◽

Artificial Neural Networks ◽

Clinical Decision Making ◽

Clinical Decision ◽

Maldescensus Testis ◽

Artificial Neural

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Applications of artificial neural networks in health care organizational decision-making: A scoping review

PLoS ONE ◽

10.1371/journal.pone.0212356 ◽

2019 ◽

Vol 14 (2) ◽

pp. e0212356 ◽

Cited By ~ 54

Author(s):

Nida Shahid ◽

Tim Rappon ◽

Whitney Berta

Keyword(s):

Neural Networks ◽

Health Care ◽

Decision Making ◽

Artificial Neural Networks ◽

Scoping Review ◽

Organizational Decision Making ◽

Artificial Neural

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Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks

Advances in Artificial Intelligence ◽

10.1155/2013/435321 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

E. Chatzimichail ◽

E. Paraskakis ◽

A. Rigas

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Clinical Decision Making ◽

Persistent Asthma ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Least Square Regression ◽

Asthma Outcome ◽

Artificial Neural

The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood.

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