{"id":"https://openalex.org/W4398183427","doi":"https://doi.org/10.1093/jamia/ocae103","title":"Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room","display_name":"Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room","publication_year":2024,"publication_date":"2024-05-21","ids":{"openalex":"https://openalex.org/W4398183427","doi":"https://doi.org/10.1093/jamia/ocae103","pmid":"https://pubmed.ncbi.nlm.nih.gov/38771093"},"language":"en","primary_location":{"id":"doi:10.1093/jamia/ocae103","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocae103","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://pmc.ncbi.nlm.nih.gov/articles/PMC11339523/pdf/ocae103.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030539003","display_name":"Benjamin S. Glicksberg","orcid":"https://orcid.org/0000-0003-4515-8090"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin S Glicksberg","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":"https://orcid.org/0000-0003-4515-8090","affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021533090","display_name":"Prem Timsina","orcid":"https://orcid.org/0000-0002-6047-887X"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prem Timsina","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033934770","display_name":"Dhaval Patel","orcid":"https://orcid.org/0000-0002-5449-6975"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhaval Patel","raw_affiliation_strings":["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020281170","display_name":"Ashwin Sawant","orcid":"https://orcid.org/0000-0003-1525-8541"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashwin Sawant","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":"https://orcid.org/0000-0003-1525-8541","affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042331995","display_name":"Akhil Vaid","orcid":"https://orcid.org/0000-0002-3343-744X"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akhil Vaid","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029443736","display_name":"Ganesh Raut","orcid":"https://orcid.org/0009-0006-7914-7065"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ganesh Raut","raw_affiliation_strings":["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091224898","display_name":"Alexander W. Charney","orcid":"https://orcid.org/0000-0001-8135-6858"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander W Charney","raw_affiliation_strings":["The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012807122","display_name":"Donald U. Apakama","orcid":"https://orcid.org/0000-0001-6217-1620"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donald Apakama","raw_affiliation_strings":["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai , New York, NY 10029, United States"],"raw_orcid":"https://orcid.org/0000-0001-6217-1620","affiliations":[{"raw_affiliation_string":"Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai , New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024282536","display_name":"Brendan G. Carr","orcid":"https://orcid.org/0000-0002-9147-9701"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brendan G Carr","raw_affiliation_strings":["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai , New York, NY 10029, United States","Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai , New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070462882","display_name":"Robert Freeman","orcid":"https://orcid.org/0000-0003-4946-6533"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Freeman","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":"https://orcid.org/0000-0003-4946-6533","affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047405500","display_name":"Girish N. Nadkarni","orcid":"https://orcid.org/0000-0001-6319-4314"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Girish N Nadkarni","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039899830","display_name":"Eyal Klang","orcid":"https://orcid.org/0000-0002-4567-3108"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eyal Klang","raw_affiliation_strings":["Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]},{"raw_affiliation_string":"The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States","institution_ids":["https://openalex.org/I98704320"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5039899830","https://openalex.org/A5047405500"],"corresponding_institution_ids":["https://openalex.org/I98704320"],"apc_list":{"value":3967,"currency":"USD","value_usd":3967},"apc_paid":null,"fwci":8.4909,"has_fulltext":true,"cited_by_count":76,"citation_normalized_percentile":{"value":0.98118757,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"31","issue":"9","first_page":"1921","last_page":"1928"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.7099000215530396,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.7099000215530396,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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":0.09399999678134918,"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"}},{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.06419999897480011,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5904542803764343},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.49188923835754395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35515373945236206},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35147106647491455},{"id":"https://openalex.org/keywords/medical-emergency","display_name":"Medical emergency","score":0.3259759545326233},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2231689989566803},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.14530035853385925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5904542803764343},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.49188923835754395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35515373945236206},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35147106647491455},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.3259759545326233},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2231689989566803},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.14530035853385925}],"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":"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":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004636","descriptor_name":"Emergency Service, Hospital","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"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":"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":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009323","descriptor_name":"Natural Language Processing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010343","descriptor_name":"Patient Admission","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057286","descriptor_name":"Electronic Health Records","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":3,"locations":[{"id":"doi:10.1093/jamia/ocae103","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocae103","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:38771093","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38771093","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:11339523","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11339523","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11339523/pdf/ocae103.pdf","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:11339523","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11339523","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11339523/pdf/ocae103.pdf","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":[{"score":0.75,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G2702327382","display_name":null,"funder_award_id":"5R01HL141841-05","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4398183427.pdf"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2161123687","https://openalex.org/W3027879771","https://openalex.org/W3200630038","https://openalex.org/W3208995063","https://openalex.org/W4200139321","https://openalex.org/W4280534587","https://openalex.org/W4312220150","https://openalex.org/W4379769651","https://openalex.org/W4384561707","https://openalex.org/W4385212751","https://openalex.org/W4388725043","https://openalex.org/W6777615688"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"BACKGROUND:":[0],"Artificial":[1],"intelligence":[2],"(AI)":[3],"and":[4,34,39,73,99,105,109,129,134,136,167,180,196,204,220],"large":[5],"language":[6],"models":[7,70,102],"(LLMs)":[8],"can":[9],"play":[10],"a":[11,85],"critical":[12],"role":[13],"in":[14,36,122,234,292],"emergency":[15,60],"room":[16],"operations":[17],"by":[18,42,76],"augmenting":[19],"decision-making":[20],"about":[21],"patient":[22,57],"admission.":[23],"However,":[24],"there":[25],"are":[26],"no":[27],"studies":[28],"for":[29,55,272,286],"LLMs":[30,280],"using":[31,88],"real-world":[32,241,282],"data":[33,190,283],"scenarios,":[35],"comparison":[37],"to":[38,67,191,212,243],"being":[40],"informed":[41,75],"traditional":[43,68,253],"supervised":[44],"machine":[45],"learning":[46],"(ML)":[47],"models.":[48,255],"We":[49,64,83,96,117],"evaluated":[50],"the":[51,150,161,263],"performance":[52,66,175,211,229],"of":[53,156,165,170,279],"GPT-4":[54,120],"predicting":[56,235],"admissions":[58,237],"from":[59,193,252],"department":[61],"(ED)":[62],"visits.":[63],"compared":[65],"ML":[69,115,138,144,198,254,265],"both":[71],"naively":[72],"when":[74,186,238],"few-shot":[77],"examples":[78,242],"and/or":[79,249],"numerical":[80,139,250],"probabilities.":[81,140],"METHODS:":[82],"conducted":[84],"retrospective":[86],"study":[87],"electronic":[89],"health":[90],"records":[91],"across":[92],"7":[93],"NYC":[94],"hospitals.":[95],"trained":[97],"Bio-Clinical-BERT":[98],"XGBoost":[100],"(XGB)":[101],"on":[103],"unstructured":[104],"structured":[106],"data,":[107],"respectively,":[108],"created":[110],"an":[111,147,158,168],"ensemble":[112],"model":[113,145],"reflecting":[114],"performance.":[116],"then":[118],"assessed":[119],"capabilities":[121],"many":[123],"scenarios:":[124],"through":[125,247],"Zero-shot,":[126],"Few-shot":[127],"with":[128,135,240,281],"without":[130,137],"retrieval-augmented":[131],"generation":[132],"(RAG),":[133],"RESULTS:":[141],"The":[142,172,224],"Ensemble":[143],"achieved":[146],"area":[148,159],"under":[149,160],"receiver":[151],"operating":[152],"characteristic":[153],"curve":[154,163],"(AUC)":[155],"0.88,":[157],"precision-recall":[162],"(AUPRC)":[164],"0.72":[166],"accuracy":[169],"82.9%.":[171],"na\u00efve":[173,225],"GPT-4's":[174],"(0.79":[176],"AUC,":[177,201,217],"0.48":[178],"AUPRC,":[179,203,219],"77.5%":[181],"accuracy)":[182],"showed":[183,231],"substantial":[184],"improvement":[185,233],"given":[187,269],"limited,":[188],"relevant":[189],"learn":[192,244],"(ie,":[194],"RAG)":[195],"underlying":[197],"probabilities":[199,251],"(0.87":[200],"0.71":[202],"83.1%":[205],"accuracy).":[206,222],"Interestingly,":[207],"RAG":[208],"alone":[209],"boosted":[210],"near":[213],"peak":[214,257],"levels":[215],"(0.82":[216],"0.56":[218],"81.3%":[221],"CONCLUSIONS:":[223],"LLM":[226],"had":[227],"limited":[228],"but":[230],"significant":[232],"ED":[236],"supplemented":[239],"from,":[245],"particularly":[246],"RAG,":[248],"Its":[256],"performance,":[258],"although":[259],"slightly":[260],"lower":[261],"than":[262],"pure":[264],"model,":[266],"is":[267,284],"noteworthy":[268],"its":[270],"potential":[271],"providing":[273],"reasoning":[274],"behind":[275],"predictions.":[276],"Further":[277],"refinement":[278],"necessary":[285],"successful":[287],"integration":[288],"as":[289],"decision-support":[290],"tools":[291],"care":[293],"settings.":[294]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":47},{"year":2024,"cited_by_count":20}],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2025-10-10T00:00:00"}
