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Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort
The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification o...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9564535/ https://www.ncbi.nlm.nih.gov/pubmed/36231500 http://dx.doi.org/10.3390/ijerph191912200 |
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author | Culié, Dorian Schiappa, Renaud Contu, Sara Scheller, Boris Villarme, Agathe Dassonville, Olivier Poissonnet, Gilles Bozec, Alexandre Chamorey, Emmanuel |
author_facet | Culié, Dorian Schiappa, Renaud Contu, Sara Scheller, Boris Villarme, Agathe Dassonville, Olivier Poissonnet, Gilles Bozec, Alexandre Chamorey, Emmanuel |
author_sort | Culié, Dorian |
collection | PubMed |
description | The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification of patients according to the pathology involved in a head and neck surgery department. 24,434 Electronic Health Records (EHR) from the first visit between 2000 and 2020 were extracted. More than 6000 EHR were manually classified in ten groups of interest according to the reason for consultation with a clinical relevance. A convolutional neural network (TensorFlow, previously reported by Hsu et al.) was then used to predict the group of patients based on their pathology, using two levels of classification based on clinically relevant criteria. On the first and second level of classification, macro-average performances were: 0.95, 0.83, 0.85, 0.97, 0.84 and 0.93, 0.76, 0.83, 0.96, 0.79 for accuracy, recall, precision, specificity and F1-score versus accuracy, recall and precision of 0.580, 580 and 0.582 for Hsu et al., respectively. We validated this model to predict the pathology involved and to constitute clinically relevant cohorts in a tertiary hospital. This model did not require a preprocessing stage, was used in French and showed equivalent or better performances than other already published techniques. |
format | Online Article Text |
id | pubmed-9564535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95645352022-10-15 Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort Culié, Dorian Schiappa, Renaud Contu, Sara Scheller, Boris Villarme, Agathe Dassonville, Olivier Poissonnet, Gilles Bozec, Alexandre Chamorey, Emmanuel Int J Environ Res Public Health Article The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification of patients according to the pathology involved in a head and neck surgery department. 24,434 Electronic Health Records (EHR) from the first visit between 2000 and 2020 were extracted. More than 6000 EHR were manually classified in ten groups of interest according to the reason for consultation with a clinical relevance. A convolutional neural network (TensorFlow, previously reported by Hsu et al.) was then used to predict the group of patients based on their pathology, using two levels of classification based on clinically relevant criteria. On the first and second level of classification, macro-average performances were: 0.95, 0.83, 0.85, 0.97, 0.84 and 0.93, 0.76, 0.83, 0.96, 0.79 for accuracy, recall, precision, specificity and F1-score versus accuracy, recall and precision of 0.580, 580 and 0.582 for Hsu et al., respectively. We validated this model to predict the pathology involved and to constitute clinically relevant cohorts in a tertiary hospital. This model did not require a preprocessing stage, was used in French and showed equivalent or better performances than other already published techniques. MDPI 2022-09-26 /pmc/articles/PMC9564535/ /pubmed/36231500 http://dx.doi.org/10.3390/ijerph191912200 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Culié, Dorian Schiappa, Renaud Contu, Sara Scheller, Boris Villarme, Agathe Dassonville, Olivier Poissonnet, Gilles Bozec, Alexandre Chamorey, Emmanuel Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title | Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title_full | Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title_fullStr | Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title_full_unstemmed | Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title_short | Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort |
title_sort | validation and improvement of a convolutional neural network to predict the involved pathology in a head and neck surgery cohort |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9564535/ https://www.ncbi.nlm.nih.gov/pubmed/36231500 http://dx.doi.org/10.3390/ijerph191912200 |
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