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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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 |
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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 |
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