Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis p...
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Autor*in:	Yingjie Su [verfasserIn] Cuirong Guo [verfasserIn] Shifang Zhou [verfasserIn] Changluo Li [verfasserIn] Ning Ding [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Artificial neural networks Sepsis Mortality MIMIC-III

Übergeordnetes Werk:	In: European Journal of Medical Research - BMC, 2012, 27(2022), 1, Seite 10
Übergeordnetes Werk:	volume:27 ; year:2022 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40001-022-00925-3

Katalog-ID:	DOAJ083308598

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520			\|a Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
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allfields	10.1186/s40001-022-00925-3 doi (DE-627)DOAJ083308598 (DE-599)DOAJffbe5eb8f1c247078b108f1a752465dc DE-627 ger DE-627 rakwb eng Yingjie Su verfasserin aut Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. Artificial neural networks Sepsis Mortality MIMIC-III Medicine R Cuirong Guo verfasserin aut Shifang Zhou verfasserin aut Changluo Li verfasserin aut Ning Ding verfasserin aut In European Journal of Medical Research BMC, 2012 27(2022), 1, Seite 10 (DE-627)375977775 (DE-600)2129989-4 2047783X nnns volume:27 year:2022 number:1 pages:10 https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc kostenfrei https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/toc/2047-783X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 27 2022 1 10
spelling	10.1186/s40001-022-00925-3 doi (DE-627)DOAJ083308598 (DE-599)DOAJffbe5eb8f1c247078b108f1a752465dc DE-627 ger DE-627 rakwb eng Yingjie Su verfasserin aut Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. Artificial neural networks Sepsis Mortality MIMIC-III Medicine R Cuirong Guo verfasserin aut Shifang Zhou verfasserin aut Changluo Li verfasserin aut Ning Ding verfasserin aut In European Journal of Medical Research BMC, 2012 27(2022), 1, Seite 10 (DE-627)375977775 (DE-600)2129989-4 2047783X nnns volume:27 year:2022 number:1 pages:10 https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc kostenfrei https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/toc/2047-783X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 27 2022 1 10
allfields_unstemmed	10.1186/s40001-022-00925-3 doi (DE-627)DOAJ083308598 (DE-599)DOAJffbe5eb8f1c247078b108f1a752465dc DE-627 ger DE-627 rakwb eng Yingjie Su verfasserin aut Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. Artificial neural networks Sepsis Mortality MIMIC-III Medicine R Cuirong Guo verfasserin aut Shifang Zhou verfasserin aut Changluo Li verfasserin aut Ning Ding verfasserin aut In European Journal of Medical Research BMC, 2012 27(2022), 1, Seite 10 (DE-627)375977775 (DE-600)2129989-4 2047783X nnns volume:27 year:2022 number:1 pages:10 https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc kostenfrei https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/toc/2047-783X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 27 2022 1 10
allfieldsGer	10.1186/s40001-022-00925-3 doi (DE-627)DOAJ083308598 (DE-599)DOAJffbe5eb8f1c247078b108f1a752465dc DE-627 ger DE-627 rakwb eng Yingjie Su verfasserin aut Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. Artificial neural networks Sepsis Mortality MIMIC-III Medicine R Cuirong Guo verfasserin aut Shifang Zhou verfasserin aut Changluo Li verfasserin aut Ning Ding verfasserin aut In European Journal of Medical Research BMC, 2012 27(2022), 1, Seite 10 (DE-627)375977775 (DE-600)2129989-4 2047783X nnns volume:27 year:2022 number:1 pages:10 https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc kostenfrei https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/toc/2047-783X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 27 2022 1 10
allfieldsSound	10.1186/s40001-022-00925-3 doi (DE-627)DOAJ083308598 (DE-599)DOAJffbe5eb8f1c247078b108f1a752465dc DE-627 ger DE-627 rakwb eng Yingjie Su verfasserin aut Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis. Artificial neural networks Sepsis Mortality MIMIC-III Medicine R Cuirong Guo verfasserin aut Shifang Zhou verfasserin aut Changluo Li verfasserin aut Ning Ding verfasserin aut In European Journal of Medical Research BMC, 2012 27(2022), 1, Seite 10 (DE-627)375977775 (DE-600)2129989-4 2047783X nnns volume:27 year:2022 number:1 pages:10 https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc kostenfrei https://doi.org/10.1186/s40001-022-00925-3 kostenfrei https://doaj.org/toc/2047-783X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 27 2022 1 10
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author	Yingjie Su
spellingShingle	Yingjie Su misc Artificial neural networks misc Sepsis misc Mortality misc MIMIC-III misc Medicine misc R Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
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topic_title	Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model Artificial neural networks Sepsis Mortality MIMIC-III
topic	misc Artificial neural networks misc Sepsis misc Mortality misc MIMIC-III misc Medicine misc R
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title	Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
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title_full	Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
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author_browse	Yingjie Su Cuirong Guo Shifang Zhou Changluo Li Ning Ding
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title_sort	early predicting 30-day mortality in sepsis in mimic-iii by an artificial neural networks model
title_auth	Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
abstract	Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
abstractGer	Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
abstract_unstemmed	Abstract Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.
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title_short	Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
url	https://doi.org/10.1186/s40001-022-00925-3 https://doaj.org/article/ffbe5eb8f1c247078b108f1a752465dc https://doaj.org/toc/2047-783X
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