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Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method

Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutiv...

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Autores principales: Wang, Wenwen, Xu, Yang, Yuan, Suzhen, Li, Zhiying, Zhu, Xin, Zhou, Qin, Shen, Wenfeng, Wang, Shixuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931475/
https://www.ncbi.nlm.nih.gov/pubmed/35308550
http://dx.doi.org/10.3389/fmed.2022.851890
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author Wang, Wenwen
Xu, Yang
Yuan, Suzhen
Li, Zhiying
Zhu, Xin
Zhou, Qin
Shen, Wenfeng
Wang, Shixuan
author_facet Wang, Wenwen
Xu, Yang
Yuan, Suzhen
Li, Zhiying
Zhu, Xin
Zhou, Qin
Shen, Wenfeng
Wang, Shixuan
author_sort Wang, Wenwen
collection PubMed
description Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.
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spelling pubmed-89314752022-03-19 Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method Wang, Wenwen Xu, Yang Yuan, Suzhen Li, Zhiying Zhu, Xin Zhou, Qin Shen, Wenfeng Wang, Shixuan Front Med (Lausanne) Medicine Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China. Frontiers Media S.A. 2022-03-04 /pmc/articles/PMC8931475/ /pubmed/35308550 http://dx.doi.org/10.3389/fmed.2022.851890 Text en Copyright © 2022 Wang, Xu, Yuan, Li, Zhu, Zhou, Shen and Wang. 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). 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 Medicine
Wang, Wenwen
Xu, Yang
Yuan, Suzhen
Li, Zhiying
Zhu, Xin
Zhou, Qin
Shen, Wenfeng
Wang, Shixuan
Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title_full Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title_fullStr Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title_full_unstemmed Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title_short Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
title_sort prediction of endometrial carcinoma using the combination of electronic health records and an ensemble machine learning method
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931475/
https://www.ncbi.nlm.nih.gov/pubmed/35308550
http://dx.doi.org/10.3389/fmed.2022.851890
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