Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who u...

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Autores principales:	Bum-Joo Cho, Kyoung Min Kim, Sanchir-Erdene Bilegsaikhan, Yong Joon Suh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f3cff78ff55490c84cc808d4185950f
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spelling	oai:doaj.org-article:2f3cff78ff55490c84cc808d4185950f2021-12-02T19:12:25ZMachine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer10.1038/s41598-020-71927-62045-2322https://doaj.org/article/2f3cff78ff55490c84cc808d4185950f2020-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-71927-6https://doaj.org/toc/2045-2322Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.Bum-Joo ChoKyoung Min KimSanchir-Erdene BilegsaikhanYong Joon SuhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bum-Joo Cho Kyoung Min Kim Sanchir-Erdene Bilegsaikhan Yong Joon Suh Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
description	Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.
format	article
author	Bum-Joo Cho Kyoung Min Kim Sanchir-Erdene Bilegsaikhan Yong Joon Suh
author_facet	Bum-Joo Cho Kyoung Min Kim Sanchir-Erdene Bilegsaikhan Yong Joon Suh
author_sort	Bum-Joo Cho
title	Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
title_short	Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
title_full	Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
title_fullStr	Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
title_full_unstemmed	Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer
title_sort	machine learning improves the prediction of febrile neutropenia in korean inpatients undergoing chemotherapy for breast cancer
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/2f3cff78ff55490c84cc808d4185950f
work_keys_str_mv	AT bumjoocho machinelearningimprovesthepredictionoffebrileneutropeniainkoreaninpatientsundergoingchemotherapyforbreastcancer AT kyoungminkim machinelearningimprovesthepredictionoffebrileneutropeniainkoreaninpatientsundergoingchemotherapyforbreastcancer AT sanchirerdenebilegsaikhan machinelearningimprovesthepredictionoffebrileneutropeniainkoreaninpatientsundergoingchemotherapyforbreastcancer AT yongjoonsuh machinelearningimprovesthepredictionoffebrileneutropeniainkoreaninpatientsundergoingchemotherapyforbreastcancer
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Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

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