Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was ap...
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Autor*in:	Xue Zhou [verfasserIn] Keijiro Nakamura [verfasserIn] Naohiko Sahara [verfasserIn] Masako Asami [verfasserIn] Yasutake Toyoda [verfasserIn] Yoshinari Enomoto [verfasserIn] Hidehiko Hara [verfasserIn] Mahito Noro [verfasserIn] Kaoru Sugi [verfasserIn] Masao Moroi [verfasserIn] Masato Nakamura [verfasserIn] Ming Huang [verfasserIn] Xin Zhu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	heart failure machine learning mortality risk patient phenotypes prognosis

Übergeordnetes Werk:	In: Life - MDPI AG, 2012, 12(2022), 6, p 776
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:6, p 776

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/life12060776

Katalog-ID:	DOAJ078942160

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520			\|a Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
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allfields	10.3390/life12060776 doi (DE-627)DOAJ078942160 (DE-599)DOAJ00d7866132d34c269dbd20a792500fb6 DE-627 ger DE-627 rakwb eng Xue Zhou verfasserin aut Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. heart failure machine learning mortality risk patient phenotypes prognosis Science Q Keijiro Nakamura verfasserin aut Naohiko Sahara verfasserin aut Masako Asami verfasserin aut Yasutake Toyoda verfasserin aut Yoshinari Enomoto verfasserin aut Hidehiko Hara verfasserin aut Mahito Noro verfasserin aut Kaoru Sugi verfasserin aut Masao Moroi verfasserin aut Masato Nakamura verfasserin aut Ming Huang verfasserin aut Xin Zhu verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 776 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 776 https://doi.org/10.3390/life12060776 kostenfrei https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 kostenfrei https://www.mdpi.com/2075-1729/12/6/776 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 6, p 776
spelling	10.3390/life12060776 doi (DE-627)DOAJ078942160 (DE-599)DOAJ00d7866132d34c269dbd20a792500fb6 DE-627 ger DE-627 rakwb eng Xue Zhou verfasserin aut Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. heart failure machine learning mortality risk patient phenotypes prognosis Science Q Keijiro Nakamura verfasserin aut Naohiko Sahara verfasserin aut Masako Asami verfasserin aut Yasutake Toyoda verfasserin aut Yoshinari Enomoto verfasserin aut Hidehiko Hara verfasserin aut Mahito Noro verfasserin aut Kaoru Sugi verfasserin aut Masao Moroi verfasserin aut Masato Nakamura verfasserin aut Ming Huang verfasserin aut Xin Zhu verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 776 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 776 https://doi.org/10.3390/life12060776 kostenfrei https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 kostenfrei https://www.mdpi.com/2075-1729/12/6/776 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 6, p 776
allfields_unstemmed	10.3390/life12060776 doi (DE-627)DOAJ078942160 (DE-599)DOAJ00d7866132d34c269dbd20a792500fb6 DE-627 ger DE-627 rakwb eng Xue Zhou verfasserin aut Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. heart failure machine learning mortality risk patient phenotypes prognosis Science Q Keijiro Nakamura verfasserin aut Naohiko Sahara verfasserin aut Masako Asami verfasserin aut Yasutake Toyoda verfasserin aut Yoshinari Enomoto verfasserin aut Hidehiko Hara verfasserin aut Mahito Noro verfasserin aut Kaoru Sugi verfasserin aut Masao Moroi verfasserin aut Masato Nakamura verfasserin aut Ming Huang verfasserin aut Xin Zhu verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 776 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 776 https://doi.org/10.3390/life12060776 kostenfrei https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 kostenfrei https://www.mdpi.com/2075-1729/12/6/776 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 6, p 776
allfieldsGer	10.3390/life12060776 doi (DE-627)DOAJ078942160 (DE-599)DOAJ00d7866132d34c269dbd20a792500fb6 DE-627 ger DE-627 rakwb eng Xue Zhou verfasserin aut Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. heart failure machine learning mortality risk patient phenotypes prognosis Science Q Keijiro Nakamura verfasserin aut Naohiko Sahara verfasserin aut Masako Asami verfasserin aut Yasutake Toyoda verfasserin aut Yoshinari Enomoto verfasserin aut Hidehiko Hara verfasserin aut Mahito Noro verfasserin aut Kaoru Sugi verfasserin aut Masao Moroi verfasserin aut Masato Nakamura verfasserin aut Ming Huang verfasserin aut Xin Zhu verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 776 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 776 https://doi.org/10.3390/life12060776 kostenfrei https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 kostenfrei https://www.mdpi.com/2075-1729/12/6/776 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 6, p 776
allfieldsSound	10.3390/life12060776 doi (DE-627)DOAJ078942160 (DE-599)DOAJ00d7866132d34c269dbd20a792500fb6 DE-627 ger DE-627 rakwb eng Xue Zhou verfasserin aut Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition. heart failure machine learning mortality risk patient phenotypes prognosis Science Q Keijiro Nakamura verfasserin aut Naohiko Sahara verfasserin aut Masako Asami verfasserin aut Yasutake Toyoda verfasserin aut Yoshinari Enomoto verfasserin aut Hidehiko Hara verfasserin aut Mahito Noro verfasserin aut Kaoru Sugi verfasserin aut Masao Moroi verfasserin aut Masato Nakamura verfasserin aut Ming Huang verfasserin aut Xin Zhu verfasserin aut In Life MDPI AG, 2012 12(2022), 6, p 776 (DE-627)718627156 (DE-600)2662250-6 20751729 nnns volume:12 year:2022 number:6, p 776 https://doi.org/10.3390/life12060776 kostenfrei https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 kostenfrei https://www.mdpi.com/2075-1729/12/6/776 kostenfrei https://doaj.org/toc/2075-1729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 12 2022 6, p 776
language	English
source	In Life 12(2022), 6, p 776 volume:12 year:2022 number:6, p 776
sourceStr	In Life 12(2022), 6, p 776 volume:12 year:2022 number:6, p 776
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	heart failure machine learning mortality risk patient phenotypes prognosis Science Q
isfreeaccess_bool	true
container_title	Life
authorswithroles_txt_mv	Xue Zhou @@aut@@ Keijiro Nakamura @@aut@@ Naohiko Sahara @@aut@@ Masako Asami @@aut@@ Yasutake Toyoda @@aut@@ Yoshinari Enomoto @@aut@@ Hidehiko Hara @@aut@@ Mahito Noro @@aut@@ Kaoru Sugi @@aut@@ Masao Moroi @@aut@@ Masato Nakamura @@aut@@ Ming Huang @@aut@@ Xin Zhu @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	718627156
id	DOAJ078942160
language_de	englisch
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author	Xue Zhou
spellingShingle	Xue Zhou misc heart failure misc machine learning misc mortality risk misc patient phenotypes misc prognosis misc Science misc Q Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
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topic_title	Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning heart failure machine learning mortality risk patient phenotypes prognosis
topic	misc heart failure misc machine learning misc mortality risk misc patient phenotypes misc prognosis misc Science misc Q
topic_unstemmed	misc heart failure misc machine learning misc mortality risk misc patient phenotypes misc prognosis misc Science misc Q
topic_browse	misc heart failure misc machine learning misc mortality risk misc patient phenotypes misc prognosis misc Science misc Q
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title	Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
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title_full	Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
author_sort	Xue Zhou
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author_browse	Xue Zhou Keijiro Nakamura Naohiko Sahara Masako Asami Yasutake Toyoda Yoshinari Enomoto Hidehiko Hara Mahito Noro Kaoru Sugi Masao Moroi Masato Nakamura Ming Huang Xin Zhu
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title_sort	exploring and identifying prognostic phenotypes of patients with heart failure guided by explainable machine learning
title_auth	Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
abstract	Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
abstractGer	Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
abstract_unstemmed	Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i<p</i< < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i<p</i< = 0.003), and 0.26 (95%CI 0.11–0.61, <i<p</i< = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
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container_issue	6, p 776
title_short	Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning
url	https://doi.org/10.3390/life12060776 https://doaj.org/article/00d7866132d34c269dbd20a792500fb6 https://www.mdpi.com/2075-1729/12/6/776 https://doaj.org/toc/2075-1729
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