{"id":"https://openalex.org/W7214034063","doi":"https://doi.org/10.1371/journal.pone.0358876","title":"Performance of machine learning\u2013based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis","display_name":"Performance of machine learning\u2013based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis","publication_year":2026,"publication_date":"2026-09-22","ids":{"openalex":"https://openalex.org/W7214034063","doi":"https://doi.org/10.1371/journal.pone.0358876","pmid":"https://pubmed.ncbi.nlm.nih.gov/42771639","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13596806"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pone.0358876","doi":"https://doi.org/10.1371/journal.pone.0358876","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0358876","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358876&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"volume":null,"type":"review","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358876&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100531448","display_name":"Jinhua Yan","orcid":"https://orcid.org/0009-0001-7422-036X","observed_orcids":["https://orcid.org/0009-0001-7422-036X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinhua Yan","raw_affiliation_strings":["the People\u2019s Hospital of Leshan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the People\u2019s Hospital of Leshan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076820487","display_name":"Yanping Song","orcid":"https://orcid.org/0000-0002-6961-994X","observed_orcids":["https://orcid.org/0000-0002-6961-994X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanping Song","raw_affiliation_strings":["the People\u2019s Hospital of Leshan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the People\u2019s Hospital of Leshan","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yangmei Du","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yangmei Du","raw_affiliation_strings":["the People\u2019s Hospital of Leshan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the People\u2019s Hospital of Leshan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001275877","display_name":"Fanmin Li","orcid":"https://orcid.org/0009-0003-6309-8121","observed_orcids":["https://orcid.org/0009-0003-6309-8121","https://orcid.org/0000-0002-8290-5422"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fanmin Li","raw_affiliation_strings":["the People\u2019s Hospital of Leshan"],"raw_orcid":"https://orcid.org/0009-0003-6309-8121","affiliations":[{"raw_affiliation_string":"the People\u2019s Hospital of Leshan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5001275877"],"corresponding_institution_ids":[],"apc_list":{"value":2382,"currency":"USD","value_usd":2382},"apc_paid":{"value":2382,"currency":"USD","value_usd":2382},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.49330914,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"9","first_page":"e0358876","last_page":"e0358876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10560","display_name":"Diabetes Management and Research","score":0.9345999956130981,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T10560","display_name":"Diabetes Management and Research","score":0.9345999956130981,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.02590000070631504,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.023900000378489494,"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/hypoglycemia-prediction","display_name":"hypoglycemia prediction","score":0.9319999814033508},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"diabetes mellitus","score":0.8040000200271606},{"id":"https://openalex.org/keywords/machine-learning-prediction-models","display_name":"machine learning prediction models","score":0.9380000233650208},{"id":"https://openalex.org/keywords/chinese-patients","display_name":"Chinese patients","score":0.8500000238418579},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.7519999742507935},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9929999709129333},{"id":"https://openalex.org/keywords/lightgbm","display_name":"LightGBM","score":0.9520000219345093},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9350000023841858},{"id":"https://openalex.org/keywords/model-validation","display_name":"model validation","score":0.4659999907016754},{"id":"https://openalex.org/keywords/risk-factors","display_name":"risk factors","score":0.4690000116825104},{"id":"https://openalex.org/keywords/insulin-use","display_name":"insulin use","score":0.6959999799728394},{"id":"https://openalex.org/keywords/hba1c","display_name":"HbA1c","score":0.9279999732971191},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.628000020980835}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7659000158309937},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6970000267028809},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6775000095367432},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6726999878883362},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.659500002861023},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6194999814033508},{"id":"https://openalex.org/C2780668416","wikidata":"https://www.wikidata.org/wiki/Q202758","display_name":"Hypoglycemia","level":3,"score":0.5990999937057495},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5975000262260437},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5611000061035156},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4681999981403351},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3160000145435333},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C76318530","wikidata":"https://www.wikidata.org/wiki/Q16833590","display_name":"Area under the curve","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C187960798","wikidata":"https://www.wikidata.org/wiki/Q7631152","display_name":"Subgroup analysis","level":3,"score":0.2903999984264374},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.2542000114917755}],"mesh":[{"descriptor_ui":"D000093743","descriptor_name":"Random Forest","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000095225","descriptor_name":"East Asian People","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098404","descriptor_name":"Boosting Machine Learning Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098412","descriptor_name":"Predictive Learning Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D002681","descriptor_name":"China","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":false},{"descriptor_ui":"D003920","descriptor_name":"Diabetes Mellitus","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":"D007003","descriptor_name":"Hypoglycemia","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D007003","descriptor_name":"Hypoglycemia","qualifier_ui":"Q000453","qualifier_name":"epidemiology","is_major_topic":true},{"descriptor_ui":"D012372","descriptor_name":"ROC Curve","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1371/journal.pone.0358876","doi":"https://doi.org/10.1371/journal.pone.0358876","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0358876","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358876&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},{"id":"pmid:42771639","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42771639","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,"listed_in":[],"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":"PloS one","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13596806","doi":null,"is_oa":true,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42771639/","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLoS One","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:9c6c992e13bb44708250c3cff1ac7028","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/9c6c992e13bb44708250c3cff1ac7028","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PLoS ONE, Vol 21, Iss 9, p e0358876 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1371/journal.pone.0358876","doi":"https://doi.org/10.1371/journal.pone.0358876","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pone.0358876","pdf_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358876&type=printable","source":{"id":"https://openalex.org/S202381698","display_name":"PLoS ONE","issn_l":"1932-6203","issn":["1932-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS One","raw_type":"journal-article"},"sustainable_development_goals":[{"score":1.0,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[{"display_name":"Systematic Review","id":"https://openalex.org/study-designs/systematic-review"},{"display_name":"Meta-Analysis","id":"https://openalex.org/study-designs/meta-analysis"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214034063.pdf","grobid_xml":"https://content.openalex.org/works/W7214034063.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1921132582","https://openalex.org/W2157823046","https://openalex.org/W2785704959","https://openalex.org/W2907554860","https://openalex.org/W2910216826","https://openalex.org/W2923407010","https://openalex.org/W3000630830","https://openalex.org/W3017000889","https://openalex.org/W3128531128","https://openalex.org/W3172011038","https://openalex.org/W4200026682","https://openalex.org/W4200088206","https://openalex.org/W4210352360","https://openalex.org/W4283008433","https://openalex.org/W4285677335","https://openalex.org/W4365443182","https://openalex.org/W4378575546","https://openalex.org/W4387012889","https://openalex.org/W4387840694","https://openalex.org/W4388517949","https://openalex.org/W4392130370","https://openalex.org/W4392518185","https://openalex.org/W4399113706","https://openalex.org/W4399319782","https://openalex.org/W4401413009","https://openalex.org/W4403142790","https://openalex.org/W4406374700","https://openalex.org/W4408356088","https://openalex.org/W4409410531","https://openalex.org/W4414300921","https://openalex.org/W4414579497","https://openalex.org/W4416027787","https://openalex.org/W4416539131","https://openalex.org/W7118706776","https://openalex.org/W7124137823"],"related_works":["https://openalex.org/W3123700022","https://openalex.org/W7162845761","https://openalex.org/W4318053822","https://openalex.org/W4285493176","https://openalex.org/W4416027787","https://openalex.org/W3035672697","https://openalex.org/W4283008433","https://openalex.org/W3021246267","https://openalex.org/W7131398323","https://openalex.org/W4402355345"],"abstract_inverted_index":{"OBJECTIVES:":[0],"This":[1],"study":[2,77],"aimed":[3],"to":[4,42,216,271,278],"systematically":[5,25],"evaluate":[6],"the":[7,32,49,92,109,127,142],"predictive":[8],"performance":[9,86,247],"of":[10,30,53,113,121,130,161,211,221],"machine":[11,177,222],"learning":[12],"(ML)-based":[13],"models":[14,55,225,243,276],"for":[15,56,163,169,174,180,187,193,226,285],"predicting":[16,57],"hypoglycemia":[17,58,131,227],"in":[18,59,228,233,258,282],"Chinese":[19,60],"patients":[20,61,229],"with":[21,62,230,244],"diabetes.":[22,63,231],"METHODS:":[23],"We":[24,214],"searched":[26],"PubMed,":[27],"Embase,":[28],"Web":[29],"Science,":[31],"Cochrane":[33],"Library,":[34],"CINAHL,":[35],"CNKI,":[36],"and":[37,66,84,126,191,209,253,265,274,277],"Wanfang":[38],"databases":[39],"from":[40],"inception":[41],"February":[43],"2026.":[44],"Eligible":[45],"studies":[46,123],"focused":[47],"on":[48,76],"development":[50],"or":[51],"validation":[52,82,255],"ML-based":[54],"Study":[64,104],"selection":[65],"data":[67],"extraction":[68],"were":[69,124,256],"performed":[70],"independently":[71],"by":[72,155],"two":[73],"reviewers.":[74],"Information":[75],"characteristics,":[78],"modeling":[79,156],"approaches,":[80],"predictors,":[81],"methods,":[83],"model":[85,263],"was":[87,98,106,132,148],"collected.":[88],"The":[89,137],"area":[90,140],"under":[91,141],"receiver":[93,143],"operating":[94,144],"characteristic":[95,145],"curve":[96,146],"(AUC)":[97,147],"synthesized":[99],"using":[100,108],"a":[101,218],"random-effects":[102],"model.":[103],"quality":[105],"assessed":[107],"Prediction":[110],"Model":[111],"Risk":[112],"Bias":[114],"Assessment":[115],"Tool":[116],"(PROBAST).":[117],"RESULTS:":[118],"A":[119],"total":[120],"13":[122],"included,":[125],"pooled":[128,139,159],"prevalence":[129],"25%":[133],"(95%":[134,150],"CI:":[135,151],"17%-33%).":[136],"overall":[138],"0.90":[149],"0.87-0.93).":[152],"Subgroup":[153],"analyses":[154],"algorithms":[157],"showed":[158],"AUCs":[160],"0.89":[162],"extreme":[164],"gradient":[165],"boosting":[166],"(XGBoost),":[167],"0.88":[168],"random":[170],"forest":[171],"(RF),":[172],"0.85":[173],"support":[175],"vector":[176],"(SVM),":[178],"0.84":[179],"Light":[181],"Gradient":[182],"Boosting":[183],"Machine":[184],"(LightGBM),":[185],"0.83":[186],"logistic":[188],"regression":[189],"(LR),":[190],"0.81":[192],"decision":[194],"tree":[195],"(DT)":[196],"models.":[197],"Common":[198],"predictors":[199],"included":[200],"age,":[201],"insulin":[202],"use,":[203],"body":[204],"mass":[205],"index,":[206],"HbA1c,":[207],"creatinine,":[208],"history":[210],"hypoglycemia.":[212],"CONCLUSION:":[213],"attempted":[215],"provide":[217],"comprehensive":[219],"overview":[220],"learning-based":[223],"prediction":[224],"Research":[232],"this":[234],"field":[235],"remains":[236],"at":[237],"an":[238],"early":[239,286],"stage,":[240],"although":[241],"several":[242],"good":[245],"discriminatory":[246],"have":[248],"been":[249],"reported.":[250],"Methodological":[251],"limitations":[252],"insufficient":[254],"observed":[257],"many":[259],"studies.":[260],"Concerns":[261],"regarding":[262],"robustness":[264],"interpretability":[266],"also":[267],"exist.":[268],"More":[269],"efforts":[270],"develop":[272],"reliable":[273],"interpretable":[275],"promote":[279],"their":[280],"application":[281],"clinical":[283],"practice":[284],"risk":[287],"identification":[288],"are":[289],"needed.":[290]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-09-23T00:00:00"}
