{"id":"https://openalex.org/W3131760363","doi":"https://doi.org/10.1093/jamia/ocaa347","title":"Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)","display_name":"Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)","publication_year":2021,"publication_date":"2021-01-27","ids":{"openalex":"https://openalex.org/W3131760363","doi":"https://doi.org/10.1093/jamia/ocaa347","mag":"3131760363","pmid":"https://pubmed.ncbi.nlm.nih.gov/33638343"},"language":"en","primary_location":{"id":"doi:10.1093/jamia/ocaa347","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocaa347","pdf_url":null,"source":{"id":"https://openalex.org/S129839026","display_name":"Journal of the American Medical Informatics Association","issn_l":"1067-5027","issn":["1067-5027","1527-974X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8661441","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091518761","display_name":"Santiago Romero\u2010Brufau","orcid":"https://orcid.org/0000-0002-9922-0083"},"institutions":[{"id":"https://openalex.org/I1330342723","display_name":"Mayo Clinic","ror":"https://ror.org/02qp3tb03","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Santiago Romero-Brufau","raw_affiliation_strings":["Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA","Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":"https://orcid.org/0000-0002-9922-0083","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I1330342723"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005973469","display_name":"Daniel Whitford","orcid":"https://orcid.org/0009-0007-6930-1636"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Whitford","raw_affiliation_strings":["Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102028360","display_name":"Matthew G. Johnson","orcid":"https://orcid.org/0000-0003-3361-0668"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew G Johnson","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070471245","display_name":"Joel Hickman","orcid":"https://orcid.org/0000-0002-6673-6358"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joel Hickman","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082299880","display_name":"Bruce W. Morlan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bruce W Morlan","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014433885","display_name":"Terry M. Therneau","orcid":"https://orcid.org/0000-0003-1490-6711"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Terry Therneau","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034994490","display_name":"James M. Naessens","orcid":"https://orcid.org/0000-0002-9151-4220"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Naessens","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA","institution_ids":["https://openalex.org/I4210146710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077182059","display_name":"Jeanne M. Huddleston","orcid":"https://orcid.org/0000-0001-9101-4122"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeanne M Huddleston","raw_affiliation_strings":["Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091518761"],"corresponding_institution_ids":["https://openalex.org/I1330342723","https://openalex.org/I136199984"],"apc_list":{"value":3967,"currency":"USD","value_usd":3967},"apc_paid":null,"fwci":6.2613,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":{"value":0.97473562,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"28","issue":"6","first_page":"1207","last_page":"1215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.007300000172108412,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/early-warning-score","display_name":"Early warning score","score":0.887778639793396},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.693663477897644},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.6319000124931335},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6279523372650146},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6263973712921143},{"id":"https://openalex.org/keywords/emergency-medicine","display_name":"Emergency medicine","score":0.5125327110290527},{"id":"https://openalex.org/keywords/intensive-care-unit","display_name":"Intensive care unit","score":0.4542527496814728},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44176003336906433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39870020747184753},{"id":"https://openalex.org/keywords/medical-emergency","display_name":"Medical emergency","score":0.35674840211868286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2586352527141571},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25826844573020935},{"id":"https://openalex.org/keywords/intensive-care-medicine","display_name":"Intensive care medicine","score":0.21344155073165894},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.13741827011108398}],"concepts":[{"id":"https://openalex.org/C2777671062","wikidata":"https://www.wikidata.org/wiki/Q6889402","display_name":"Early warning score","level":2,"score":0.887778639793396},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.693663477897644},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6319000124931335},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6279523372650146},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6263973712921143},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.5125327110290527},{"id":"https://openalex.org/C2776376669","wikidata":"https://www.wikidata.org/wiki/Q5094647","display_name":"Intensive care unit","level":2,"score":0.4542527496814728},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44176003336906433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39870020747184753},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.35674840211868286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2586352527141571},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25826844573020935},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.21344155073165894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.13741827011108398},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000080162","descriptor_name":"Early Warning Score","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000080162","descriptor_name":"Early Warning Score","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000080162","descriptor_name":"Early Warning Score","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006760","descriptor_name":"Hospitalization","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006760","descriptor_name":"Hospitalization","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006760","descriptor_name":"Hospitalization","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007297","descriptor_name":"Inpatients","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007297","descriptor_name":"Inpatients","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007297","descriptor_name":"Inpatients","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007362","descriptor_name":"Intensive Care Units","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007362","descriptor_name":"Intensive Care Units","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007362","descriptor_name":"Intensive Care Units","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1093/jamia/ocaa347","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocaa347","pdf_url":null,"source":{"id":"https://openalex.org/S129839026","display_name":"Journal of the American Medical Informatics Association","issn_l":"1067-5027","issn":["1067-5027","1527-974X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},{"id":"pmid:33638343","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33638343","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association : JAMIA","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:8661441","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8661441","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Am Med Inform Assoc","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:8661441","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8661441","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Am Med Inform Assoc","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/16"},{"display_name":"Reduced inequalities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W1921733975","https://openalex.org/W1923712662","https://openalex.org/W1961125951","https://openalex.org/W1993397663","https://openalex.org/W1994763846","https://openalex.org/W2004290125","https://openalex.org/W2012048170","https://openalex.org/W2029598996","https://openalex.org/W2032916871","https://openalex.org/W2039251546","https://openalex.org/W2039695998","https://openalex.org/W2050915224","https://openalex.org/W2058407709","https://openalex.org/W2070080552","https://openalex.org/W2070493638","https://openalex.org/W2070607910","https://openalex.org/W2081811104","https://openalex.org/W2096978103","https://openalex.org/W2101134120","https://openalex.org/W2102332328","https://openalex.org/W2124538018","https://openalex.org/W2128962082","https://openalex.org/W2129231390","https://openalex.org/W2150979970","https://openalex.org/W2265226819","https://openalex.org/W2333561512","https://openalex.org/W2750557731","https://openalex.org/W2770222727","https://openalex.org/W2782643792","https://openalex.org/W2811095531","https://openalex.org/W2889301483","https://openalex.org/W2891853753","https://openalex.org/W2899364832","https://openalex.org/W2910910290","https://openalex.org/W2945471000","https://openalex.org/W2945543078","https://openalex.org/W2964244079","https://openalex.org/W2968723626","https://openalex.org/W2968919852","https://openalex.org/W2970794262","https://openalex.org/W3001393923","https://openalex.org/W3001777144","https://openalex.org/W6773012587"],"related_works":["https://openalex.org/W3200179079","https://openalex.org/W2790196630","https://openalex.org/W4383891492","https://openalex.org/W2994841944","https://openalex.org/W2150979970","https://openalex.org/W2968586400","https://openalex.org/W2942650110","https://openalex.org/W2971899301","https://openalex.org/W3131760363","https://openalex.org/W2023015132"],"abstract_inverted_index":{"OBJECTIVE:":[0],"We":[1],"aimed":[2],"to":[3,48],"develop":[4],"a":[5,29,70,171,249],"model":[6],"for":[7,79,92,199,210],"accurate":[8],"prediction":[9,106,238],"of":[10,107,173,201,239],"general":[11,50,212,240],"care":[12,51,57,72,109,213,241],"inpatient":[13,52,242],"deterioration.":[14],"MATERIALS":[15],"AND":[16],"METHODS:":[17],"Training":[18],"and":[19,40,87,95,119,128,147,156,230,248],"internal":[20,117,146],"validation":[21,121,149],"datasets":[22,122,150],"were":[23,77,90,158],"built":[24],"using":[25,244],"2-year":[26],"data":[27],"from":[28,69],"quaternary":[30],"hospital":[31,73],"in":[32,65,74,143,160,167],"the":[33,75,98,145,161,188,211],"Midwest.":[34],"Model":[35],"training":[36],"used":[37,78],"gradient":[38],"boosting":[39],"feature":[41,232,246],"engineering":[42,247],"(clinically":[43],"relevant":[44],"interactions,":[45],"time-series":[46],"information)":[47],"predict":[49],"deterioration":[53,243],"(resuscitation":[54],"call,":[55],"intensive":[56,108],"unit":[58,110],"transfer,":[59],"or":[60,112],"rapid":[61],"response":[62],"team":[63],"call)":[64],"24":[66,124],"hours.":[67],"Data":[68],"tertiary":[71],"Southwest":[76],"external":[80,120,148,168],"validation.":[81],"C-statistic,":[82],"sensitivity,":[83],"positive":[84],"predictive":[85],"value,":[86],"alert":[88,195,203,254],"rate":[89],"calculated":[91],"different":[93],"cutoffs":[94],"compared":[96,220],"with":[97,221],"National":[99,189],"Early":[100,136,190],"Warning":[101,137,191],"Score.":[102,192],"Sensitivity":[103],"analysis":[104,163],"evaluated":[105],"transfer":[111],"resuscitation":[113],"call.":[114],"RESULTS:":[115],"Training,":[116],"validation,":[118],"included":[123],"500,":[125],"25":[126],"784":[127],"53":[129],"956":[130],"hospitalizations,":[131],"respectively.":[132],"The":[133],"Mayo":[134],"Clinic":[135],"Score":[138],"(MC-EWS)":[139],"demonstrated":[140],"excellent":[141],"discrimination":[142,218],"both":[144,227],"(C-statistic":[151,164],"=":[152,165],"0.913,":[153],"0.937,":[154],"respectively),":[155],"results":[157],"consistent":[159],"sensitivity":[162,172],"0.932":[166],"validation).":[169],"At":[170],"73%,":[174],"MC-EWS":[175,225,235],"would":[176],"generate":[177],"0.7":[178],"alerts":[179],"per":[180,182],"day":[181],"10":[183],"patients,":[184],"45%":[185],"less":[186],"than":[187],"DISCUSSION:":[193],"Low":[194],"rates":[196],"are":[197],"important":[198],"implementation":[200],"an":[202],"system.":[204],"Other":[205],"early":[206],"warning":[207],"scores":[208],"developed":[209],"ward":[214],"have":[215],"achieved":[216,236],"lower":[217],"overall":[219],"MC-EWS,":[222],"likely":[223],"because":[224],"includes":[226],"nursing":[228],"assessments":[229],"extensive":[231],"engineering.":[233],"CONCLUSIONS:":[234],"superior":[237],"sophisticated":[245],"machine":[250],"learning":[251],"approach,":[252],"reducing":[253],"rate.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}