Cargando…

Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms

BACKGROUND: Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. METHODS: This retrospective cross-sectional study in...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Dougho, Cho, Jae Man, Yang, Joong Won, Yang, Donghoon, Kim, Mansu, Oh, Gayeoul, Kwon, Heum Dai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9485547/
https://www.ncbi.nlm.nih.gov/pubmed/36147698
http://dx.doi.org/10.3389/fsurg.2022.1010420
_version_ 1784792092898230272
author Park, Dougho
Cho, Jae Man
Yang, Joong Won
Yang, Donghoon
Kim, Mansu
Oh, Gayeoul
Kwon, Heum Dai
author_facet Park, Dougho
Cho, Jae Man
Yang, Joong Won
Yang, Donghoon
Kim, Mansu
Oh, Gayeoul
Kwon, Heum Dai
author_sort Park, Dougho
collection PubMed
description BACKGROUND: Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. METHODS: This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification. RESULTS: In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors. CONCLUSION: ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.
format Online
Article
Text
id pubmed-9485547
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94855472022-09-21 Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms Park, Dougho Cho, Jae Man Yang, Joong Won Yang, Donghoon Kim, Mansu Oh, Gayeoul Kwon, Heum Dai Front Surg Surgery BACKGROUND: Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. METHODS: This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification. RESULTS: In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors. CONCLUSION: ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database. Frontiers Media S.A. 2022-09-06 /pmc/articles/PMC9485547/ /pubmed/36147698 http://dx.doi.org/10.3389/fsurg.2022.1010420 Text en © 2022 Park, Cho, Yang, Yang, Kim, Oh and Kwon. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Surgery
Park, Dougho
Cho, Jae Man
Yang, Joong Won
Yang, Donghoon
Kim, Mansu
Oh, Gayeoul
Kwon, Heum Dai
Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title_full Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title_fullStr Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title_full_unstemmed Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title_short Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
title_sort classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9485547/
https://www.ncbi.nlm.nih.gov/pubmed/36147698
http://dx.doi.org/10.3389/fsurg.2022.1010420
work_keys_str_mv AT parkdougho classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT chojaeman classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT yangjoongwon classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT yangdonghoon classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT kimmansu classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT ohgayeoul classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms
AT kwonheumdai classificationofexpertleveltherapeuticdecisionsfordegenerativecervicalmyelopathyusingensemblemachinelearningalgorithms