Validation and Improvement of a Convolutional Neural Network to Predict the Involved Pathology in a Head and Neck Surgery Cohort

Dorian Culié (), Renaud Schiappa, Sara Contu, Boris Scheller, Agathe Villarme, Olivier Dassonville, Gilles Poissonnet, Alexandre Bozec and Emmanuel Chamorey
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Dorian Culié: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Renaud Schiappa: Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
Sara Contu: Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France
Boris Scheller: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Agathe Villarme: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Olivier Dassonville: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Gilles Poissonnet: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Alexandre Bozec: Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France
Emmanuel Chamorey: Epidemiology, Biostatistics and Health Data Department, Antoine Laccassagne Center, 06100 Nice, France

IJERPH, 2022, vol. 19, issue 19, 1-10

Abstract: 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.

Keywords: neural network; cohort constitution; patient classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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