{"id":"https://openalex.org/W4385443777","doi":"https://doi.org/10.1093/jamia/ocad152","title":"Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning","display_name":"Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385443777","doi":"https://doi.org/10.1093/jamia/ocad152","pmid":"https://pubmed.ncbi.nlm.nih.gov/37528056"},"language":"en","primary_location":{"id":"doi:10.1093/jamia/ocad152","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jamia/ocad152","pdf_url":"https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocad152/51020035/ocad152.pdf","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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":"hybrid","oa_url":"https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocad152/51020035/ocad152.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089104211","display_name":"Yuqing Mao","orcid":"https://orcid.org/0000-0003-4573-6567"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuqing Mao","raw_affiliation_strings":["National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA","institution_ids":["https://openalex.org/I2800548410","https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069987722","display_name":"Randolph A. Miller","orcid":null},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Randolph A Miller","raw_affiliation_strings":["National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA","institution_ids":["https://openalex.org/I2800548410","https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073807945","display_name":"Olivier Bodenreider","orcid":"https://orcid.org/0000-0003-4769-4217"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olivier Bodenreider","raw_affiliation_strings":["National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA","institution_ids":["https://openalex.org/I2800548410","https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066629222","display_name":"Vinh Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vinh Nguyen","raw_affiliation_strings":["National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA","institution_ids":["https://openalex.org/I2800548410","https://openalex.org/I1299303238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031594710","display_name":"Kin Wah Fung","orcid":"https://orcid.org/0000-0003-0593-5377"},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]},{"id":"https://openalex.org/I2800548410","display_name":"United States National Library of Medicine","ror":"https://ror.org/0060t0j89","country_code":"US","type":"archive","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I2800548410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kin Wah Fung","raw_affiliation_strings":["National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA"],"raw_orcid":"https://orcid.org/0000-0003-0593-5377","affiliations":[{"raw_affiliation_string":"National Library of Medicine, National Institutes of Health , Bethesda, Maryland, USA","institution_ids":["https://openalex.org/I2800548410","https://openalex.org/I1299303238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031594710"],"corresponding_institution_ids":["https://openalex.org/I1299303238","https://openalex.org/I2800548410"],"apc_list":{"value":3967,"currency":"USD","value_usd":3967},"apc_paid":{"value":3967,"currency":"USD","value_usd":3967},"fwci":0.8722,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74629735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"30","issue":"12","first_page":"1887","last_page":"1894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9176999926567078,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9176999926567078,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.027300000190734863,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.007600000128149986,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/unified-medical-language-system","display_name":"Unified Medical Language System","score":0.8549410104751587},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.799485445022583},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.7951507568359375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7513307332992554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6330175399780273},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5996266603469849},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5738136768341064},{"id":"https://openalex.org/keywords/atom","display_name":"Atom (system on chip)","score":0.4282373785972595},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3429356813430786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12630566954612732},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.10381734371185303}],"concepts":[{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.8549410104751587},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.799485445022583},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.7951507568359375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513307332992554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6330175399780273},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5996266603469849},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5738136768341064},{"id":"https://openalex.org/C58312451","wikidata":"https://www.wikidata.org/wiki/Q4817200","display_name":"Atom (system on chip)","level":2,"score":0.4282373785972595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3429356813430786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12630566954612732},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.10381734371185303},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000066506","descriptor_name":"Heuristics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000066506","descriptor_name":"Heuristics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000066506","descriptor_name":"Heuristics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000066506","descriptor_name":"Heuristics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000066506","descriptor_name":"Heuristics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017432","descriptor_name":"Unified Medical Language System","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017432","descriptor_name":"Unified Medical Language System","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017432","descriptor_name":"Unified Medical Language System","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017432","descriptor_name":"Unified Medical Language System","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017432","descriptor_name":"Unified Medical Language System","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1093/jamia/ocad152","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jamia/ocad152","pdf_url":"https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocad152/51020035/ocad152.pdf","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},{"id":"pmid:37528056","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37528056","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:10654847","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10654847","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10654847/pdf/ocad152.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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Am Med Inform Assoc","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1093/jamia/ocad152","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jamia/ocad152","pdf_url":"https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocad152/51020035/ocad152.pdf","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.44999998807907104,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385443777.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W153781075","https://openalex.org/W207212328","https://openalex.org/W1042295638","https://openalex.org/W1519436992","https://openalex.org/W1596936080","https://openalex.org/W1965466476","https://openalex.org/W1971974040","https://openalex.org/W2026227080","https://openalex.org/W2049998732","https://openalex.org/W2081888868","https://openalex.org/W2084413241","https://openalex.org/W2115871101","https://openalex.org/W2159583324","https://openalex.org/W2165957204","https://openalex.org/W2604748391","https://openalex.org/W2775175954","https://openalex.org/W2795453480","https://openalex.org/W2944400536","https://openalex.org/W3043067930","https://openalex.org/W3048364465","https://openalex.org/W3153694186","https://openalex.org/W4224313128","https://openalex.org/W4285242074","https://openalex.org/W4385443777","https://openalex.org/W6677115753","https://openalex.org/W6719902200","https://openalex.org/W6747513068","https://openalex.org/W6929580945"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W4387678054","https://openalex.org/W3095575180","https://openalex.org/W2389596151","https://openalex.org/W4221148444","https://openalex.org/W4226054107","https://openalex.org/W79619734","https://openalex.org/W2010487328","https://openalex.org/W2340589664","https://openalex.org/W4281750475"],"abstract_inverted_index":{"OBJECTIVE:":[0],"Use":[1],"heuristic,":[2],"deep":[3],"learning":[4],"(DL),":[5],"and":[6,69,81,158,243],"hybrid":[7,149,189],"AI":[8,202],"methods":[9,127,152,203,242,245],"to":[10,60,128,215,231],"predict":[11,205],"semantic":[12],"group":[13],"(SG)":[14],"assignments":[15,207],"for":[16,54,72,77,84,119,136,171,208,237],"new":[17,175,209,229],"UMLS":[18,35,210,232],"Metathesaurus":[19,36],"atoms,":[20],"with":[21,111,212],"target":[22],"accuracy":[23,180,194,214],"\u226595%.":[24],"MATERIALS":[25],"AND":[26],"METHODS:":[27],"We":[28,96,234],"used":[29],"train-test":[30],"datasets":[31],"from":[32],"successive":[33],"2020AA-2022AB":[34],"releases.":[37],"Our":[38,198],"heuristic":[39,157,163,241],"\"waterfall\"":[40],"approach":[41,66,165],"employed":[42],"a":[43,55,98,105],"sequence":[44],"of":[45,87,91,100,115,156,169,195,227],"7":[46],"different":[47],"SG":[48,135,150,206,238],"prediction":[49,151],"methods.":[50,160],"Atoms":[51],"not":[52],"qualifying":[53],"method":[56],"were":[57],"passed":[58],"on":[59,143,181],"the":[61,88,101,130,154,182,224],"next":[62],"method.":[63],"The":[64,162,178,188],"DL":[65,159,179,244],"generated":[67],"BioWordVec":[68,75,82],"SapBERT":[70],"embeddings":[71,76,83,103],"atom":[73,85,138],"names,":[74,80],"source":[78,94],"vocabulary":[79],"names":[86],"second-to-top":[89],"nodes":[90,117],"an":[92,112,137,192,220],"atom's":[93],"hierarchy.":[95],"fed":[97],"concatenation":[99],"4":[102],"into":[104],"fully":[106],"connected":[107],"multilayer":[108],"neural":[109],"network":[110],"output":[113],"layer":[114],"15":[116],"(one":[118],"each":[120],"SG).":[121],"For":[122],"both":[123],"approaches,":[124],"we":[125,146],"developed":[126,147],"estimate":[129],"probability":[131],"that":[132,201,236],"their":[133],"predicted":[134,167],"would":[139],"be":[140,216],"correct.":[141],"Based":[142],"these":[144],"estimations,":[145],"2":[148],"combining":[153,240],"strengths":[155],"RESULTS:":[161],"waterfall":[164],"accurately":[166],"94.3%":[168],"SGs":[170],"1":[172],"563":[173],"692":[174],"unseen":[176],"atoms.":[177],"same":[183],"dataset":[184],"was":[185],"also":[186],"94.3%.":[187],"approaches":[190],"achieved":[191],"average":[193],"96.5%.":[196],"CONCLUSION:":[197],"study":[199],"demonstrated":[200],"can":[204,246],"atoms":[211,230],"sufficient":[213],"potentially":[217],"useful":[218],"as":[219],"intermediate":[221],"step":[222],"in":[223],"time-consuming":[225],"task":[226],"assigning":[228],"concepts.":[233],"showed":[235],"prediction,":[239],"produce":[247],"better":[248],"results":[249],"than":[250],"either":[251],"alone.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
