Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divide...
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Veröffentlicht in: | Journal of personalized medicine 2022-11, Vol.12 (11), p.1930 |
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creator | Yu, Chenyan Li, Yao Yin, Minyue Gao, Jingwen Xi, Liting Lin, Jiaxi Liu, Lu Zhang, Huixian Wu, Airong Xu, Chunfang Liu, Xiaolin Wang, Yue Zhu, Jinzhou |
description | Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application. |
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fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9693570</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2748294783</sourcerecordid><originalsourceid>FETCH-LOGICAL-c386t-76b6cac4481a2b9406bdaa9f5d4d035b0662d51362440d12f222446c974fc7513</originalsourceid><addsrcrecordid>eNpdkctKxTAQhoMoKurKFyi4EaSaW9NmI8jxCsfLQtchTVKbQ5sck1Q5b2-8IOoQmJn8Hz8zDAD7CB4TwuHJYjkijBDiBK6BbQzrqqQUs_Vf9RbYi3EBczQVxgxugi3CKMYIVttAn03JjzIZXdxK1VtnirmRwVn3XFhXPASjrUofHYHluVwVtz4kOdi0-pRlssalWLzZ1Bd33pWz3g8mpvyvipkNoffRxl2w0ckhmr3vvAOeLi8eZ9fl_P7qZnY2LxVpWCpr1jIlFaUNkrjlFLJWS8m7SlMNSdVCxrCuEGGYUqgR7jDOFVO8pp2qs7ADTr98l1M7Gq3yaEEOYhnsKMNKeGnFX8XZXjz7V8EZJ1UNs8Hht0HwL1PeQ4w2KjMM0hk_RYFrwvNrapzRg3_owk_B5fUyRRvMad2QTB19USr4GIPpfoZBUHwcUPw6IHkH3mSL3w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2748294783</pqid></control><display><type>article</type><title>Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>PubMed Central Open Access</source><creator>Yu, Chenyan ; Li, Yao ; Yin, Minyue ; Gao, Jingwen ; Xi, Liting ; Lin, Jiaxi ; Liu, Lu ; Zhang, Huixian ; Wu, Airong ; Xu, Chunfang ; Liu, Xiaolin ; Wang, Yue ; Zhu, Jinzhou</creator><creatorcontrib>Yu, Chenyan ; Li, Yao ; Yin, Minyue ; Gao, Jingwen ; Xi, Liting ; Lin, Jiaxi ; Liu, Lu ; Zhang, Huixian ; Wu, Airong ; Xu, Chunfang ; Liu, Xiaolin ; Wang, Yue ; Zhu, Jinzhou</creatorcontrib><description>Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.</description><identifier>ISSN: 2075-4426</identifier><identifier>EISSN: 2075-4426</identifier><identifier>DOI: 10.3390/jpm12111930</identifier><identifier>PMID: 36422105</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Ascites ; Automation ; Bilirubin ; Cholangitis ; Cholesterol ; Cirrhosis ; Creatinine ; Datasets ; Deep learning ; Gallbladder diseases ; Hepatitis ; High density lipoprotein ; Hospitals ; Laboratories ; Learning algorithms ; Liver cirrhosis ; Liver diseases ; Machine learning ; Medical prognosis ; Mortality ; Patients ; Precision medicine ; Regression analysis ; Trends ; Variables</subject><ispartof>Journal of personalized medicine, 2022-11, Vol.12 (11), p.1930</ispartof><rights>2022 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-76b6cac4481a2b9406bdaa9f5d4d035b0662d51362440d12f222446c974fc7513</citedby><cites>FETCH-LOGICAL-c386t-76b6cac4481a2b9406bdaa9f5d4d035b0662d51362440d12f222446c974fc7513</cites><orcidid>0000-0003-4171-7457 ; 0000-0002-9772-4608 ; 0000-0003-0544-9248 ; 0000-0002-3724-5744</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693570/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693570/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids></links><search><creatorcontrib>Yu, Chenyan</creatorcontrib><creatorcontrib>Li, Yao</creatorcontrib><creatorcontrib>Yin, Minyue</creatorcontrib><creatorcontrib>Gao, Jingwen</creatorcontrib><creatorcontrib>Xi, Liting</creatorcontrib><creatorcontrib>Lin, Jiaxi</creatorcontrib><creatorcontrib>Liu, Lu</creatorcontrib><creatorcontrib>Zhang, Huixian</creatorcontrib><creatorcontrib>Wu, Airong</creatorcontrib><creatorcontrib>Xu, Chunfang</creatorcontrib><creatorcontrib>Liu, Xiaolin</creatorcontrib><creatorcontrib>Wang, Yue</creatorcontrib><creatorcontrib>Zhu, Jinzhou</creatorcontrib><title>Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis</title><title>Journal of personalized medicine</title><description>Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.</description><subject>Algorithms</subject><subject>Ascites</subject><subject>Automation</subject><subject>Bilirubin</subject><subject>Cholangitis</subject><subject>Cholesterol</subject><subject>Cirrhosis</subject><subject>Creatinine</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Gallbladder diseases</subject><subject>Hepatitis</subject><subject>High density lipoprotein</subject><subject>Hospitals</subject><subject>Laboratories</subject><subject>Learning algorithms</subject><subject>Liver cirrhosis</subject><subject>Liver diseases</subject><subject>Machine learning</subject><subject>Medical prognosis</subject><subject>Mortality</subject><subject>Patients</subject><subject>Precision medicine</subject><subject>Regression analysis</subject><subject>Trends</subject><subject>Variables</subject><issn>2075-4426</issn><issn>2075-4426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkctKxTAQhoMoKurKFyi4EaSaW9NmI8jxCsfLQtchTVKbQ5sck1Q5b2-8IOoQmJn8Hz8zDAD7CB4TwuHJYjkijBDiBK6BbQzrqqQUs_Vf9RbYi3EBczQVxgxugi3CKMYIVttAn03JjzIZXdxK1VtnirmRwVn3XFhXPASjrUofHYHluVwVtz4kOdi0-pRlssalWLzZ1Bd33pWz3g8mpvyvipkNoffRxl2w0ckhmr3vvAOeLi8eZ9fl_P7qZnY2LxVpWCpr1jIlFaUNkrjlFLJWS8m7SlMNSdVCxrCuEGGYUqgR7jDOFVO8pp2qs7ADTr98l1M7Gq3yaEEOYhnsKMNKeGnFX8XZXjz7V8EZJ1UNs8Hht0HwL1PeQ4w2KjMM0hk_RYFrwvNrapzRg3_owk_B5fUyRRvMad2QTB19USr4GIPpfoZBUHwcUPw6IHkH3mSL3w</recordid><startdate>20221119</startdate><enddate>20221119</enddate><creator>Yu, Chenyan</creator><creator>Li, Yao</creator><creator>Yin, Minyue</creator><creator>Gao, Jingwen</creator><creator>Xi, Liting</creator><creator>Lin, Jiaxi</creator><creator>Liu, Lu</creator><creator>Zhang, Huixian</creator><creator>Wu, Airong</creator><creator>Xu, Chunfang</creator><creator>Liu, Xiaolin</creator><creator>Wang, Yue</creator><creator>Zhu, Jinzhou</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4171-7457</orcidid><orcidid>https://orcid.org/0000-0002-9772-4608</orcidid><orcidid>https://orcid.org/0000-0003-0544-9248</orcidid><orcidid>https://orcid.org/0000-0002-3724-5744</orcidid></search><sort><creationdate>20221119</creationdate><title>Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis</title><author>Yu, Chenyan ; Li, Yao ; Yin, Minyue ; Gao, Jingwen ; Xi, Liting ; Lin, Jiaxi ; Liu, Lu ; Zhang, Huixian ; Wu, Airong ; Xu, Chunfang ; Liu, Xiaolin ; Wang, Yue ; Zhu, Jinzhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-76b6cac4481a2b9406bdaa9f5d4d035b0662d51362440d12f222446c974fc7513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Ascites</topic><topic>Automation</topic><topic>Bilirubin</topic><topic>Cholangitis</topic><topic>Cholesterol</topic><topic>Cirrhosis</topic><topic>Creatinine</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Gallbladder diseases</topic><topic>Hepatitis</topic><topic>High density lipoprotein</topic><topic>Hospitals</topic><topic>Laboratories</topic><topic>Learning algorithms</topic><topic>Liver cirrhosis</topic><topic>Liver diseases</topic><topic>Machine learning</topic><topic>Medical prognosis</topic><topic>Mortality</topic><topic>Patients</topic><topic>Precision medicine</topic><topic>Regression analysis</topic><topic>Trends</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Chenyan</creatorcontrib><creatorcontrib>Li, Yao</creatorcontrib><creatorcontrib>Yin, Minyue</creatorcontrib><creatorcontrib>Gao, Jingwen</creatorcontrib><creatorcontrib>Xi, Liting</creatorcontrib><creatorcontrib>Lin, Jiaxi</creatorcontrib><creatorcontrib>Liu, Lu</creatorcontrib><creatorcontrib>Zhang, Huixian</creatorcontrib><creatorcontrib>Wu, Airong</creatorcontrib><creatorcontrib>Xu, Chunfang</creatorcontrib><creatorcontrib>Liu, Xiaolin</creatorcontrib><creatorcontrib>Wang, Yue</creatorcontrib><creatorcontrib>Zhu, Jinzhou</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of personalized medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Chenyan</au><au>Li, Yao</au><au>Yin, Minyue</au><au>Gao, Jingwen</au><au>Xi, Liting</au><au>Lin, Jiaxi</au><au>Liu, Lu</au><au>Zhang, Huixian</au><au>Wu, Airong</au><au>Xu, Chunfang</au><au>Liu, Xiaolin</au><au>Wang, Yue</au><au>Zhu, Jinzhou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis</atitle><jtitle>Journal of personalized medicine</jtitle><date>2022-11-19</date><risdate>2022</risdate><volume>12</volume><issue>11</issue><spage>1930</spage><pages>1930-</pages><issn>2075-4426</issn><eissn>2075-4426</eissn><abstract>Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.5:1.5. Models were developed on the H2O AutoML platform in the training dataset, and then were evaluated in the validation dataset by area under receiver operating characteristic curves (AUC). The best AutoML model was interpreted by SHapley Additive exPlanation (SHAP) Plot, Partial Dependence Plots (PDP), and Local Interpretable Model Agnostic Explanation (LIME). Results: The model, based on the extreme gradient boosting (XGBoost) algorithm, performed better (AUC 0.888) than the other AutoML models (logistic regression 0.673, gradient boost machine 0.886, random forest 0.866, deep learning 0.830, stacking 0.850), as well as the existing scorings (the model of end-stage liver disease [MELD] score 0.778, MELD-Na score 0.782, and albumin-bilirubin [ALBI] score 0.662). The most key variable in the XGBoost model was high-density lipoprotein cholesterol, followed by creatinine, white blood cell count, international normalized ratio, etc. Conclusion: The AutoML model based on the XGBoost algorithm presented better performance than the existing scoring systems for predicting 30-day mortality in patients with non-cholestatic cirrhosis. It shows the promise of AutoML in its future medical application.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>36422105</pmid><doi>10.3390/jpm12111930</doi><orcidid>https://orcid.org/0000-0003-4171-7457</orcidid><orcidid>https://orcid.org/0000-0002-9772-4608</orcidid><orcidid>https://orcid.org/0000-0003-0544-9248</orcidid><orcidid>https://orcid.org/0000-0002-3724-5744</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Ascites Automation Bilirubin Cholangitis Cholesterol Cirrhosis Creatinine Datasets Deep learning Gallbladder diseases Hepatitis High density lipoprotein Hospitals Laboratories Learning algorithms Liver cirrhosis Liver diseases Machine learning Medical prognosis Mortality Patients Precision medicine Regression analysis Trends Variables |
title | Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis |
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