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An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios
Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700766/ https://www.ncbi.nlm.nih.gov/pubmed/34943525 http://dx.doi.org/10.3390/diagnostics11122288 |
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author | Su, Kaixiang Wu, Jiao Gu, Dongxiao Yang, Shanlin Deng, Shuyuan Khakimova, Aida K. |
author_facet | Su, Kaixiang Wu, Jiao Gu, Dongxiao Yang, Shanlin Deng, Shuyuan Khakimova, Aida K. |
author_sort | Su, Kaixiang |
collection | PubMed |
description | Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis. |
format | Online Article Text |
id | pubmed-8700766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87007662021-12-24 An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios Su, Kaixiang Wu, Jiao Gu, Dongxiao Yang, Shanlin Deng, Shuyuan Khakimova, Aida K. Diagnostics (Basel) Article Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis. MDPI 2021-12-07 /pmc/articles/PMC8700766/ /pubmed/34943525 http://dx.doi.org/10.3390/diagnostics11122288 Text en © 2021 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 Su, Kaixiang Wu, Jiao Gu, Dongxiao Yang, Shanlin Deng, Shuyuan Khakimova, Aida K. An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title | An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title_full | An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title_fullStr | An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title_full_unstemmed | An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title_short | An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios |
title_sort | adaptive deep ensemble learning method for dynamic evolving diagnostic task scenarios |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700766/ https://www.ncbi.nlm.nih.gov/pubmed/34943525 http://dx.doi.org/10.3390/diagnostics11122288 |
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