Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed ba...
Uloženo v:
| Vydáno v: | Diagnostics (Basel) Ročník 11; číslo 11; s. 2119 |
|---|---|
| Hlavní autoři: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
15.11.2021
MDPI |
| Témata: | |
| ISSN: | 2075-4418, 2075-4418 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia. |
|---|---|
| AbstractList | The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.BACKGROUNDThe objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed.METHODSConsensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed.In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality.RESULTSIn hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality.Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.CONCLUSIONOur cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia. Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia. |
| Author | Cheungpasitporn, Wisit Kattah, Andrea G. Sy-Go, Janina Paula T. Qureshi, Fawad Vallabhajosyula, Saraschandra Dumancas, Carissa Y. Garovic, Vesna D. Pattharanitima, Pattharawin Erickson, Stephen B. Mao, Michael A. Keddis, Mira T. Nissaisorakarn, Voravech Thongprayoon, Charat Dillon, John J. |
| AuthorAffiliation | 2 Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 01702, USA; voravech.niss@gmail.com 1 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; charat.thongprayoon@gmail.com (C.T.); sy-go.janina@mayo.edu (J.P.T.S.-G.); dumancas.carissa@mayo.edu (C.Y.D.); kattah.andrea@mayo.edu (A.G.K.); Qureshi.Fawad@mayo.edu (F.Q.); garovic.Vesna@mayo.edu (V.D.G.); dillon.John@mayo.edu (J.J.D.); erickson.stephen@mayo.edu (S.B.E.) 5 Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; svallabh@wakehealth.edu 3 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA; keddis.Mira@mayo.edu 4 Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12121, Thailand; pattharawin@hotmail.com 6 Division of Nephrology and Hypertension, Departm |
| AuthorAffiliation_xml | – name: 5 Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; svallabh@wakehealth.edu – name: 1 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; charat.thongprayoon@gmail.com (C.T.); sy-go.janina@mayo.edu (J.P.T.S.-G.); dumancas.carissa@mayo.edu (C.Y.D.); kattah.andrea@mayo.edu (A.G.K.); Qureshi.Fawad@mayo.edu (F.Q.); garovic.Vesna@mayo.edu (V.D.G.); dillon.John@mayo.edu (J.J.D.); erickson.stephen@mayo.edu (S.B.E.) – name: 3 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA; keddis.Mira@mayo.edu – name: 6 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 85054, USA; mao.michael@mayo.edu – name: 4 Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12121, Thailand; pattharawin@hotmail.com – name: 2 Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 01702, USA; voravech.niss@gmail.com |
| Author_xml | – sequence: 1 givenname: Charat surname: Thongprayoon fullname: Thongprayoon, Charat – sequence: 2 givenname: Janina Paula T. surname: Sy-Go fullname: Sy-Go, Janina Paula T. – sequence: 3 givenname: Voravech orcidid: 0000-0002-9389-073X surname: Nissaisorakarn fullname: Nissaisorakarn, Voravech – sequence: 4 givenname: Carissa Y. surname: Dumancas fullname: Dumancas, Carissa Y. – sequence: 5 givenname: Mira T. orcidid: 0000-0001-8249-0848 surname: Keddis fullname: Keddis, Mira T. – sequence: 6 givenname: Andrea G. surname: Kattah fullname: Kattah, Andrea G. – sequence: 7 givenname: Pattharawin orcidid: 0000-0002-6010-0033 surname: Pattharanitima fullname: Pattharanitima, Pattharawin – sequence: 8 givenname: Saraschandra orcidid: 0000-0002-1631-8238 surname: Vallabhajosyula fullname: Vallabhajosyula, Saraschandra – sequence: 9 givenname: Michael A. orcidid: 0000-0003-1814-7003 surname: Mao fullname: Mao, Michael A. – sequence: 10 givenname: Fawad surname: Qureshi fullname: Qureshi, Fawad – sequence: 11 givenname: Vesna D. surname: Garovic fullname: Garovic, Vesna D. – sequence: 12 givenname: John J. surname: Dillon fullname: Dillon, John J. – sequence: 13 givenname: Stephen B. surname: Erickson fullname: Erickson, Stephen B. – sequence: 14 givenname: Wisit orcidid: 0000-0001-9954-9711 surname: Cheungpasitporn fullname: Cheungpasitporn, Wisit |
| BookMark | eNp9Uk1v1DAQtVArWkp_AZdIXLgs-CuOfUGqtoVWWlQOcOJgOc5416vEDnYCan89DlshWiHmYmv83ps343mBjkIMgNArgt8ypvC7zpttiHnyNpMSlBD1DJ1S3NQrzok8-ut-gs5z3uMSijBJ6-fohHFJFRfNKfr2ydidD1BtwKTgw7Zax5Ah5DlX637OE6QleTGOKRZk5WKqrmMe_WR6fw9d9dlMHsKUq59-2lWXd3koxiDD4M1LdOxMn-H84TxDXz9cfVlfrza3H2_WF5uV5Y2YVsQ13NQM1yCpU7JzAivlrONgqbCKQgusY0KKTjnlBJEApVthOgWcSW7ZGbo56HbR7PWY_GDSnY7G69-JmLbapDKpHjQzrcRUiZYzxlvVGNfy1uKO1oIsdYvW-4PWOLcDdLa0lkz_SPTxS_A7vY0_tBRE1WQRePMgkOL3GfKkB58t9L0JEOesqcAc04ZzUaCvn0D3cU6hjGpBUcwYE7Sg2AFlU8w5gftjhmC97IL-xy4UlnrCsuXLJh8X177_L_cX5ay_rA |
| CitedBy_id | crossref_primary_10_1016_j_xkme_2025_101030 crossref_primary_10_1080_0886022X_2023_2170244 crossref_primary_10_3390_jpm12121992 crossref_primary_10_1186_s12872_023_03380_y crossref_primary_10_1093_ckj_sfaf175 crossref_primary_10_3390_ani15081169 crossref_primary_10_3390_medicina58121831 crossref_primary_10_1038_s41598_024_74920_5 crossref_primary_10_1155_2023_6650620 |
| Cites_doi | 10.1093/bioinformatics/btr597 10.1093/ndtplus/sfr163 10.1038/s41598-021-96616-w 10.1007/s41666-018-0029-6 10.3390/nu12061836 10.1080/00325481.2021.1931369 10.1016/bs.acc.2015.10.002 10.1152/physrev.00012.2014 10.1080/21548331.2020.1724723 10.1023/A:1010933404324 10.3109/0886022X.2015.1057471 10.3390/diagnostics11040727 10.1016/j.cmpb.2021.106040 10.1016/j.ejim.2015.05.013 10.3390/ijerph18041919 10.3390/diagnostics11101924 10.3390/jcm9041107 10.1093/bioinformatics/btq170 10.1309/JR9Y-PPTX-AJTC-QDRD 10.1016/j.amjmed.2016.08.033 10.1038/s41408-021-00452-0 10.3390/jcm10194441 10.1097/MEG.0000000000001598 10.1016/j.jcin.2021.08.034 10.1097/SHK.0000000000000769 10.1093/ajcp/79.3.348 10.3109/0886022X.2015.1074519 10.1139/gen-2020-0131 10.1080/10934529.2020.1809925 10.2147/IJNRD.S42054 10.3109/07435800.2015.1094088 10.1007/s40620-021-01163-2 10.1038/srep06207 10.3390/diagnostics11101909 10.1023/A:1023949509487 10.1111/imj.12682 10.1371/journal.pone.0057720 10.3390/medsci8030037 10.1016/j.jbi.2019.103364 10.1136/bmjebm-2019-111322 10.1016/S0009-8981(99)00110-2 10.3945/ajcn.112.053132 10.3109/0886022X.2015.1057800 10.3390/diagnostics11101933 10.1080/17843286.2018.1516173 10.1111/ijcp.12696 10.3390/diagnostics11101908 10.1016/j.amjms.2017.08.013 10.1378/chest.95.2.257 10.1186/s12944-021-01475-z 10.3390/diagnostics11101880 10.1016/j.mayocp.2015.04.023 10.5220/0009891900980102 10.1097/00003246-198709000-00002 |
| ContentType | Journal Article |
| Copyright | 2021 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. 2021 by the authors. 2021 |
| Copyright_xml | – notice: 2021 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. – notice: 2021 by the authors. 2021 |
| DBID | AAYXX CITATION 3V. 7XB 8FK 8G5 ABUWG AFKRA AZQEC BENPR CCPQU COVID DWQXO GNUQQ GUQSH M2O MBDVC PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.3390/diagnostics11112119 |
| DatabaseName | CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College Coronavirus Research Database ProQuest Central ProQuest Central Student Research Library Prep (ProQuest) Research Library Research Library (Corporate) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Research Library Prep ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Basic ProQuest Central Essentials ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Central (Alumni Edition) ProQuest One Community College Research Library (Alumni Edition) ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Research Library ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2075-4418 |
| ExternalDocumentID | oai_doaj_org_article_3ab80296b4334b97afb4bc0d2561f609 PMC8619519 10_3390_diagnostics11112119 |
| GeographicLocations | United States--US |
| GeographicLocations_xml | – name: United States--US |
| GroupedDBID | 53G 5VS 8G5 AADQD AAFWJ AAYXX ABDBF ABUWG ACUHS ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BCNDV BENPR BPHCQ CCPQU CITATION DWQXO EBD ESX GNUQQ GROUPED_DOAJ GUQSH HYE IAO IHR ITC KQ8 M2O M48 MODMG M~E OK1 PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC RPM 3V. 7XB 8FK COVID MBDVC PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c476t-1f74a5305e82f98df6099fcf4ec26c92ebe3d3686d9f9f618ee1196ad9e4384c3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000725845600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2075-4418 |
| IngestDate | Fri Oct 03 12:53:06 EDT 2025 Tue Nov 04 02:01:21 EST 2025 Fri Sep 05 13:53:46 EDT 2025 Mon Jun 30 04:29:29 EDT 2025 Tue Nov 18 21:24:42 EST 2025 Sat Nov 29 07:17:31 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| License | 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/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c476t-1f74a5305e82f98df6099fcf4ec26c92ebe3d3686d9f9f618ee1196ad9e4384c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6010-0033 0000-0003-1814-7003 0000-0001-9954-9711 0000-0002-9389-073X 0000-0002-1631-8238 0000-0001-8249-0848 |
| OpenAccessLink | https://doaj.org/article/3ab80296b4334b97afb4bc0d2561f609 |
| PMID | 34829467 |
| PQID | 2602033362 |
| PQPubID | 2032410 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3ab80296b4334b97afb4bc0d2561f609 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8619519 proquest_miscellaneous_2604027446 proquest_journals_2602033362 crossref_primary_10_3390_diagnostics11112119 crossref_citationtrail_10_3390_diagnostics11112119 |
| PublicationCentury | 2000 |
| PublicationDate | 20211115 |
| PublicationDateYYYYMMDD | 2021-11-15 |
| PublicationDate_xml | – month: 11 year: 2021 text: 20211115 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Diagnostics (Basel) |
| PublicationYear | 2021 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Michailidis (ref_40) 2014; 4 Alyousef (ref_42) 2018; 2 MacEachern (ref_22) 2021; 64 Huijgen (ref_44) 2000; 114 ref_12 ref_11 Cheungpasitporn (ref_16) 2015; 37 Cheungpasitporn (ref_14) 2020; 48 Monti (ref_39) 2003; 52 Bokhari (ref_56) 2018; 355 Glasdam (ref_1) 2016; 73 Cheungpasitporn (ref_3) 2015; 45 Cheungpasitporn (ref_6) 2016; 41 Cheungpasitporn (ref_2) 2015; 37 ref_15 Imamura (ref_54) 2013; 98 Majoni (ref_47) 2018; 2018 Naksuk (ref_18) 2017; 130 (ref_57) 2019; 74 Yoon (ref_33) 2021; 11 ref_25 Wong (ref_9) 1983; 79 ref_23 ref_21 Wang (ref_43) 2020; 102 ref_29 ref_28 Lachmann (ref_32) 2021; 14 ref_27 ref_26 Zaloga (ref_46) 1987; 15 Jiang (ref_52) 2017; 47 ref_36 ref_35 Jiang (ref_24) 2021; 204 Cao (ref_55) 1999; 285 ref_30 Thongprayoon (ref_17) 2015; 69 Zaloga (ref_45) 1989; 95 Limaye (ref_51) 2011; 59 Hoenderop (ref_5) 2015; 95 Safavi (ref_53) 2007; 19 Cheungpasitporn (ref_8) 2015; 90 Cheungpasitporn (ref_19) 2020; 32 Gile (ref_50) 2021; 11 Thongprayoon (ref_10) 2015; 37 Wilkerson (ref_34) 2010; 26 Stekhoven (ref_41) 2012; 28 Breiman (ref_38) 2001; 45 Ketteler (ref_20) 2012; 5 Nedyalkova (ref_31) 2020; 55 Thongprayoon (ref_49) 2020; 25 Haider (ref_13) 2015; 26 Xue (ref_37) 2021; 20 ref_4 ref_7 Pham (ref_48) 2014; 7 |
| References_xml | – volume: 28 start-page: 112 year: 2012 ident: ref_41 article-title: MissForest—Non-parametric missing value imputation for mixed-type data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr597 – volume: 2018 start-page: 9041694 year: 2018 ident: ref_47 article-title: Magnesium and Human Health: Perspectives and Research Directions publication-title: Int. J. Endocrinol. – volume: 5 start-page: i3 year: 2012 ident: ref_20 article-title: Magnesium basics publication-title: Clin. Kidney J. doi: 10.1093/ndtplus/sfr163 – volume: 11 start-page: 17121 year: 2021 ident: ref_33 article-title: Differential progression of coronary atherosclerosis according to plaque composition: A cluster analysis of PARADIGM registry data publication-title: Sci. Rep. doi: 10.1038/s41598-021-96616-w – volume: 2 start-page: 402 year: 2018 ident: ref_42 article-title: Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease publication-title: J. Healthc. Inform. Res. doi: 10.1007/s41666-018-0029-6 – ident: ref_11 doi: 10.3390/nu12061836 – ident: ref_15 doi: 10.1080/00325481.2021.1931369 – volume: 73 start-page: 169 year: 2016 ident: ref_1 article-title: The Importance of Magnesium in the Human Body: A Systematic Literature Review publication-title: Adv. Clin. Chem. doi: 10.1016/bs.acc.2015.10.002 – volume: 95 start-page: 1 year: 2015 ident: ref_5 article-title: Magnesium in man: Implications for health and disease publication-title: Physiol. Rev. doi: 10.1152/physrev.00012.2014 – volume: 48 start-page: 80 year: 2020 ident: ref_14 article-title: Impact of admission serum magnesium levels on long-term mortality in hospitalized patients publication-title: Hosp. Pract. (1995) doi: 10.1080/21548331.2020.1724723 – volume: 45 start-page: 5 year: 2001 ident: ref_38 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 37 start-page: 1175 year: 2015 ident: ref_16 article-title: Admission hypomagnesemia and hypermagnesemia increase the risk of acute kidney injury publication-title: Ren. Fail. doi: 10.3109/0886022X.2015.1057471 – ident: ref_7 doi: 10.3390/diagnostics11040727 – volume: 204 start-page: 106040 year: 2021 ident: ref_24 article-title: An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission publication-title: Comput. Methods Progr. Biomed. doi: 10.1016/j.cmpb.2021.106040 – volume: 26 start-page: 504 year: 2015 ident: ref_13 article-title: Hypermagnesemia is a strong independent risk factor for mortality in critically ill patients: Results from a cross-sectional study publication-title: Eur. J. Intern. Med. doi: 10.1016/j.ejim.2015.05.013 – ident: ref_30 doi: 10.3390/ijerph18041919 – ident: ref_26 doi: 10.3390/diagnostics11101924 – ident: ref_21 doi: 10.3390/jcm9041107 – volume: 26 start-page: 1572 year: 2010 ident: ref_34 article-title: ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq170 – volume: 114 start-page: 688 year: 2000 ident: ref_44 article-title: Magnesium levels in critically ill patients. What should we measure? publication-title: Am. J. Clin. Pathol. doi: 10.1309/JR9Y-PPTX-AJTC-QDRD – volume: 130 start-page: 229.e213 year: 2017 ident: ref_18 article-title: Association of Serum Magnesium on Mortality in Patients Admitted to the Intensive Cardiac Care Unit publication-title: Am. J. Med. doi: 10.1016/j.amjmed.2016.08.033 – volume: 11 start-page: 65 year: 2021 ident: ref_50 article-title: Hypomagnesemia at the time of autologous stem cell transplantation for patients with diffuse large B-cell lymphoma is associated with an increased risk of failure publication-title: Blood Cancer J. doi: 10.1038/s41408-021-00452-0 – ident: ref_36 doi: 10.3390/jcm10194441 – volume: 32 start-page: 650 year: 2020 ident: ref_19 article-title: Hepatitis A hospitalizations among kidney transplant recipients in the United States: Nationwide inpatient sample 2005-2014 publication-title: Eur. J. Gastroenterol. Hepatol. doi: 10.1097/MEG.0000000000001598 – volume: 14 start-page: 2127 year: 2021 ident: ref_32 article-title: Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data publication-title: JACC Cardiovasc. Interv. doi: 10.1016/j.jcin.2021.08.034 – volume: 47 start-page: 288 year: 2017 ident: ref_52 article-title: Does Hypomagnesemia Impact on the Outcome of Patients Admitted to the Intensive Care Unit? A Systematic Review and Meta-Analysis publication-title: Shock doi: 10.1097/SHK.0000000000000769 – volume: 79 start-page: 348 year: 1983 ident: ref_9 article-title: A high prevalence of hypomagnesemia and hypermagnesemia in hospitalized patients publication-title: Am. J. Clin. Pathol. doi: 10.1093/ajcp/79.3.348 – volume: 37 start-page: 1518 year: 2015 ident: ref_10 article-title: Admission hypomagnesemia linked to septic shock in patients with systemic inflammatory response syndrome publication-title: Ren. Fail. doi: 10.3109/0886022X.2015.1074519 – volume: 64 start-page: 416 year: 2021 ident: ref_22 article-title: Machine learning for precision medicine publication-title: Genome doi: 10.1139/gen-2020-0131 – volume: 55 start-page: 1450 year: 2020 ident: ref_31 article-title: Fuzzy partitioning of clinical data for DMT2 patients publication-title: J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng. doi: 10.1080/10934529.2020.1809925 – volume: 7 start-page: 219 year: 2014 ident: ref_48 article-title: Hypomagnesemia: A clinical perspective publication-title: Int. J. Nephrol. Renovasc. Dis. doi: 10.2147/IJNRD.S42054 – volume: 41 start-page: 142 year: 2016 ident: ref_6 article-title: Hypomagnesemia linked to new-onset diabetes mellitus after kidney transplantation: A systematic review and meta-analysis publication-title: Endocr. Res. doi: 10.3109/07435800.2015.1094088 – volume: 19 start-page: 645 year: 2007 ident: ref_53 article-title: Admission hypomagnesemia--impact on mortality or morbidity in critically ill patients publication-title: Middle East. J. Anaesthesiol. – ident: ref_35 doi: 10.1007/s40620-021-01163-2 – volume: 4 start-page: 6207 year: 2014 ident: ref_40 article-title: Critical limitations of consensus clustering in class discovery publication-title: Sci. Rep. doi: 10.1038/srep06207 – ident: ref_28 doi: 10.3390/diagnostics11101909 – volume: 52 start-page: 91 year: 2003 ident: ref_39 article-title: Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data publication-title: Mach. Learn. doi: 10.1023/A:1023949509487 – volume: 45 start-page: 436 year: 2015 ident: ref_3 article-title: Hypomagnesaemia linked to depression: A systematic review and meta-analysis publication-title: Intern. Med. J. doi: 10.1111/imj.12682 – ident: ref_4 doi: 10.1371/journal.pone.0057720 – ident: ref_12 doi: 10.3390/medsci8030037 – volume: 102 start-page: 103364 year: 2020 ident: ref_43 article-title: Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2019.103364 – volume: 25 start-page: 206 year: 2020 ident: ref_49 article-title: Association of serum magnesium level change with in-hospital mortality publication-title: BMJ Evid. Based Med. doi: 10.1136/bmjebm-2019-111322 – volume: 285 start-page: 191 year: 1999 ident: ref_55 article-title: Acute hypermagnesemia and respiratory arrest following infusion of MgSO4 for tocolysis publication-title: Clin. Chim. Acta doi: 10.1016/S0009-8981(99)00110-2 – volume: 98 start-page: 160 year: 2013 ident: ref_54 article-title: Circulating and dietary magnesium and risk of cardiovascular disease: A systematic review and meta-analysis of prospective studies publication-title: Am. J. Clin. Nutr. doi: 10.3945/ajcn.112.053132 – volume: 37 start-page: 1237 year: 2015 ident: ref_2 article-title: Proton pump inhibitors linked to hypomagnesemia: A systematic review and meta-analysis of observational studies publication-title: Ren. Fail. doi: 10.3109/0886022X.2015.1057800 – volume: 59 start-page: 19 year: 2011 ident: ref_51 article-title: Hypomagnesemia in critically ill medical patients publication-title: J. Assoc. Physicians India – ident: ref_25 doi: 10.3390/diagnostics11101933 – volume: 74 start-page: 41 year: 2019 ident: ref_57 article-title: Hypomagnesemia and hypermagnesemia publication-title: Acta Clin. Belg. doi: 10.1080/17843286.2018.1516173 – volume: 69 start-page: 1303 year: 2015 ident: ref_17 article-title: Admission serum magnesium levels and the risk of acute respiratory failure publication-title: Int. J. Clin. Pract. doi: 10.1111/ijcp.12696 – ident: ref_27 doi: 10.3390/diagnostics11101908 – volume: 355 start-page: 390 year: 2018 ident: ref_56 article-title: Fatal Hypermagnesemia Due to Laxative Use publication-title: Am. J. Med. Sci. doi: 10.1016/j.amjms.2017.08.013 – volume: 95 start-page: 257 year: 1989 ident: ref_45 article-title: Interpretation of the serum magnesium level publication-title: Chest doi: 10.1378/chest.95.2.257 – volume: 20 start-page: 48 year: 2021 ident: ref_37 article-title: Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles publication-title: Lipids Health Dis. doi: 10.1186/s12944-021-01475-z – ident: ref_29 doi: 10.3390/diagnostics11101880 – volume: 90 start-page: 1001 year: 2015 ident: ref_8 article-title: Dysmagnesemia in Hospitalized Patients: Prevalence and Prognostic Importance publication-title: Mayo Clin. Proc. doi: 10.1016/j.mayocp.2015.04.023 – ident: ref_23 doi: 10.5220/0009891900980102 – volume: 15 start-page: 813 year: 1987 ident: ref_46 article-title: A simple method for determining physiologically active calcium and magnesium concentrations in critically ill patients publication-title: Crit. Care Med. doi: 10.1097/00003246-198709000-00002 |
| SSID | ssj0000913825 |
| Score | 2.2430358 |
| Snippet | Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised... The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 2119 |
| SubjectTerms | Age Algorithms Artificial intelligence Cardiovascular disease Clinical outcomes Cluster analysis clustering Comorbidity consensus clustering Diabetes dysmagnesemia electrolytes Hospitalization Hospitals Hypertension hypomagnesemia Laboratories Machine learning Mortality Patients Signal transduction Variables |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7BFiEuvBFpCzISR6JubK9jn1BbWnHpaoVAqsQhcvwoK7XZstlFgl_fmcS7JRLqhWtsK47HM_5mPPkG4D2ieB88_RwzCSKXiJlzIySliXkvIu4aXtiu2EQ5nerzczNLAbc2pVVubGJnqP3CUYz8AHE3HwuB9vbj9c-cqkbR7WoqoXEfdoipTI5g5-hkOvuyjbIQ6yX6QD3dkED__sD3GWzEgUzWgoYNjqSOuX8AN4fJkn-dPqdP_nfeT-Fxwp3ssN8oz-BeaJ7Dw7N0s_4Cvp91WZWBJcLVC0alPKkORsuOL9dEp0APDxMFOUOsyzY1R-Z_gmeznqC1ZRTZZZ9-t1e4AqENV3P7Er6dnnw9_pynwgu5k6Va5UUspZ2gJQiaR6N9VIgjo4syOK6c4Sh44YXSyptooip0CLiMynoTpNDSiVcwahZNeA3M6JrzUNQhyoksojOlVa6OJiBQGmtrM-Cbta9cYiWn4hiXFXonJLDqHwLL4MN20HVPynF39yMS6rYrMWp3DxbLiyopaCVsrcfcqFoKIWucZqxl7cYeIWFB35_B_ka-VVLztroVbgbvts2ooHTrYpuwWHd9ZMfDqDIoB1tpMKFhSzP_0VF9a_RvEWPv3v3yPXjEKdWGshMn-zBaLdfhDTxwv1bzdvk26cQNwuIbZw priority: 102 providerName: ProQuest |
| Title | Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia |
| URI | https://www.proquest.com/docview/2602033362 https://www.proquest.com/docview/2604027446 https://pubmed.ncbi.nlm.nih.gov/PMC8619519 https://doaj.org/article/3ab80296b4334b97afb4bc0d2561f609 |
| Volume | 11 |
| WOSCitedRecordID | wos000725845600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2075-4418 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913825 issn: 2075-4418 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2075-4418 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913825 issn: 2075-4418 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2075-4418 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913825 issn: 2075-4418 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Research Library customDbUrl: eissn: 2075-4418 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913825 issn: 2075-4418 databaseCode: M2O dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2075-4418 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913825 issn: 2075-4418 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB5BQYhLxVMNLSsjcSTqxnYc-9iWVnDYJUIgLeIQOX7ASm1aNbtI8OuZSbKrjYTgwiUHx5Hs8Yznm3j8DcBrRPE-eLockweRSsTMqRGS0sS8FxG1hme2KzZRzOd6sTDlTqkvygnr6YF7wR0LW-spN6qWQsjaFDbWsnZTj646i6q_uoeoZyeY6vZgQ9x6eU8zJDCuP_Z95hpxH9MuQcRmI1fUMfaPYOY4SXLH61w8gv0BLrKTfpiP4U5onsCD2XAg_hS-zrpkyMAGntRvjCpwUvmKlp1drokFgRpPBuZwhhCVbUqFLH8Fz8qeV7Vl9EOWvf3ZXuEEQhuulvYZfL44_3T2Lh3qJaROFmqVZrGQNkcDDppHoz2Jx0QXZXBcOcNxvYQXSitvookq0yGgFJT1JkihpRPPYa-5bsIBMKNrzkNWhyhzmUWHcleujiYgvplqaxPgG9FVbiATp5oWlxUGFSTv6g_yTuDN9qObnkvj791PaU22XYkIu2tA9agG9aj-pR4JHG1WtBqss60whuNTIdB3J_Bq-xrtig5LbBOu110f2dEnqgSKkSaMBjR-0yy_dwzdGsNShMYv_scMDuEhpzwaSj3Mj2BvdbsOL-G--7FatrcTuFss9ATunZ7Py4-TzgjwOeMfsK18Pyu__AYn1RD3 |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtNAFL0qKQI2vFENBQYJdli1ZybOzAKh0lI1ahNlUaQiFsaeRxupdUqcgMpH8Y3c60fAEuquC7b2-DGe4-tzx3fOAXiNLN46S4tj-k6EEjlzqIWkMjFrhUfU8DirzCYG47E6PtaTNfjVroWhsso2JlaB2s4MzZFvIe_mkRAYb99ffAvJNYr-rrYWGjUsDtzlD0zZynfDXRzfN5zvfTza2Q8bV4HQyEGyCGM_kFkfYe4U91pZnyBJ8sZLZ3hiNMdeCSsSlVjttU9i5VyMMM2sdlIoaQSe9wasSwS76sH6ZDiafF7N6pDKJuZctbyREDrasnXFHGkuU3QiQbXOJ7ByCujQ225x5l9fu717_9tzug93G17NtusX4QGsueIh3Bo1lQOP4Muoqhp1rBGUPWFkVUo-HyXbOVuSXARt3G4k1hlyedZ6qkx_OssmtQBtyWjmmu1eluf4xF3pzqfZY_h0LV17Ar1iVrgNYFrlnLs4d172ZeyNHmSJyb12SAQjlWUB8HasU9OorpP5x1mK2RcBJP0HQAJ4uzroohYdubr5BwLRqikphlcbZvOTtAlAqchyFXGd5FIImeNt-lzmJrJIeWPqfwCbLZ7SJoyV6R8wBfBqtRsDEP1Vygo3W1ZtZKUzmQQw6EC3c0PdPcX0tJIyV5i_Yw7x9OqLv4Tb-0ejw_RwOD54Bnc4lRVRJWZ_E3qL-dI9h5vm-2Jazl807yODr9cN7d8imXlu |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qBVVseCNSChgJdkST2J7EXiBUOoyoSodZgFSJRUj8KCO1mXYyAyqfxtdxbx4DkVB3XbBNnIeTk-t77ZNzAF5gFm-dpZ9jhk6EEnPmUAtJNDFrhUfU8DivzSbSyUQdHenpBvzq_oUhWmUXE-tAbeeG5sgHmHfzSAiMtwPf0iKmo_Gbs_OQHKRopbWz02ggcuAufmD5Vr3eH-G7fsn5-N2nvfdh6zAQGpkmyzD2qcyHCHmnuNfK-gQTJm-8dIYnRnPsobAiUYnVXvskVs7FCNncaieFkkbgea_B9VTioEy0Qf5xPb9DeptYfTVCR0LoaGAb7hypL1OcImm13mBYewb0Et0-TfOvcW98-39-YnfgVptts93m87gLG668B1uHLZ_gPnw5rLmkjrUys8eMDEzJ_aNieycrEpGgjbut8DrDDJ91Tiuzn86yaSNLWzGaz2aji-oUn76r3OksfwCfr6RrD2GznJfuETCtCs5dXDgvhzL2Rqd5YgqvHaaHkcrzAHj33jPTarGTJchJhjUZgSX7B1gCeLU-6KyRIrm8-VsC1Lop6YjXG-aL46wNS5nICxVxnRRSCFngbfpCFiaymAjH1P8AdjpsZW1wq7I_wArg-Xo3hiVaa8pLN1_VbWStPpkEkPZg3Luh_p5y9q0WOFdY1WNlsX35xZ_BFuI5-7A_OXgMNzlxjYieOdyBzeVi5Z7ADfN9OasWT-sPk8HXq8b1b7mOgKg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+Learning+Consensus+Clustering+Approach+for+Hospitalized+Patients+with+Dysmagnesemia&rft.jtitle=Diagnostics+%28Basel%29&rft.au=Thongprayoon%2C+Charat&rft.au=Sy-Go%2C+Janina+Paula+T&rft.au=Nissaisorakarn%2C+Voravech&rft.au=Dumancas%2C+Carissa+Y&rft.date=2021-11-15&rft.issn=2075-4418&rft.eissn=2075-4418&rft.volume=11&rft.issue=11&rft_id=info:doi/10.3390%2Fdiagnostics11112119&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2075-4418&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2075-4418&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2075-4418&client=summon |