{"id":"https://openalex.org/W7223797133","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","title":"[Construction of predictive models for type 2 diabetic retinopathy based on the principle of differentiation of syndrome elements].","display_name":"[Construction of predictive models for type 2 diabetic retinopathy based on the principle of differentiation of syndrome elements].","publication_year":2026,"publication_date":"2026-10-20","ids":{"openalex":"https://openalex.org/W7223797133","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","pmid":"https://pubmed.ncbi.nlm.nih.gov/42934107","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13710989"},"language":"zh","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13710989","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/pdf/nfykdxxb-46-10-2456.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,"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":"Nan Fang Yi Ke Da Xue Xue Bao","raw_type":"Text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/pdf/nfykdxxb-46-10-2456.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047934203","display_name":"Shijie Qiao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I118783713","display_name":"Fujian University of Traditional Chinese Medicine","ror":"https://ror.org/05n0qbd70","country_code":"CN","type":"education","lineage":["https://openalex.org/I118783713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijie Qiao","raw_affiliation_strings":["School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China","institution_ids":["https://openalex.org/I118783713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159962443","display_name":"Jinshun You","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I118783713","display_name":"Fujian University of Traditional Chinese Medicine","ror":"https://ror.org/05n0qbd70","country_code":"CN","type":"education","lineage":["https://openalex.org/I118783713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinshun You","raw_affiliation_strings":["School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China","institution_ids":["https://openalex.org/I118783713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102759725","display_name":"Chang Wang","orcid":"https://orcid.org/0000-0002-1905-6261","observed_orcids":["https://orcid.org/0000-0002-1905-6261"]},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]},{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210144482","display_name":"Shanghai Sixth People's Hospital","ror":"https://ror.org/049zrh188","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210144482"]}],"countries":["CN","MO"],"is_corresponding":false,"raw_author_name":"Chang Wang","raw_affiliation_strings":["Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China","Faculty of Medicine, Macau University of Science and Technology, Macao 999078, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China","institution_ids":["https://openalex.org/I183067930","https://openalex.org/I4210144482"]},{"raw_affiliation_string":"Faculty of Medicine, Macau University of Science and Technology, Macao 999078, China","institution_ids":["https://openalex.org/I111950717"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101314473","display_name":"Hongping Xiong","orcid":"https://orcid.org/0000-0002-7313-2907","observed_orcids":["https://orcid.org/0000-0002-7313-2907"]},"institutions":[{"id":"https://openalex.org/I4210121071","display_name":"Fuzhou Second Hospital","ror":"https://ror.org/02t4nzq07","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210121071"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongping Xiong","raw_affiliation_strings":["Department of Endocrinology, Fuzhou Second Hospital, Fuzhou 350007, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Endocrinology, Fuzhou Second Hospital, Fuzhou 350007, China","institution_ids":["https://openalex.org/I4210121071"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063182433","display_name":"S. Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I118783713","display_name":"Fujian University of Traditional Chinese Medicine","ror":"https://ror.org/05n0qbd70","country_code":"CN","type":"education","lineage":["https://openalex.org/I118783713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunan Li","raw_affiliation_strings":["School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China","institution_ids":["https://openalex.org/I118783713"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87830031,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"46","issue":"10","first_page":"2456","last_page":"2470"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.7494999766349792,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.7494999766349792,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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":0.12039999663829803,"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/T10170","display_name":"Retinal Diseases and Treatments","score":0.039900001138448715,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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/type-2-diabetic-retinopathy","display_name":"type 2 diabetic retinopathy","score":0.9940000176429749},{"id":"https://openalex.org/keywords/type-2-diabetes","display_name":"type 2 diabetes","score":0.9520000219345093},{"id":"https://openalex.org/keywords/syndrome-differentiation","display_name":"syndrome differentiation","score":0.8299999833106995},{"id":"https://openalex.org/keywords/disease-prediction-model","display_name":"disease prediction model","score":0.7630000114440918},{"id":"https://openalex.org/keywords/lasso-feature-selection","display_name":"LASSO feature selection","score":0.8489999771118164},{"id":"https://openalex.org/keywords/machine-learning-prediction-models","display_name":"machine learning prediction models","score":0.6169999837875366},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.7580000162124634},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.9449999928474426},{"id":"https://openalex.org/keywords/net-benefit","display_name":"net benefit","score":0.7459999918937683}],"concepts":[{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.697700023651123},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.6118000149726868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5888000130653381},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.5864999890327454},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4724000096321106},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.415800005197525},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39989998936653137},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C2778313320","wikidata":"https://www.wikidata.org/wiki/Q550455","display_name":"Retinopathy","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2694999873638153},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.251800000667572},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.25040000677108765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2500999867916107}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","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":false},{"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":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003430","descriptor_name":"Cross-Sectional Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000150","qualifier_name":"complications","is_major_topic":true},{"descriptor_ui":"D003924","descriptor_name":"Diabetes Mellitus, Type 2","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D003930","descriptor_name":"Diabetic Retinopathy","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008516","descriptor_name":"Medicine, Chinese Traditional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:13710989","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/pdf/nfykdxxb-46-10-2456.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,"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":"Nan Fang Yi Ke Da Xue Xue Bao","raw_type":"Text"},{"id":"pmid:42934107","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42934107","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":"Nan fang yi ke da xue xue bao = Journal of Southern Medical University","raw_type":"English Abstract"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13710989","doi":"https://doi.org/10.12122/j.issn.1673-4254.2026.10.15","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13710989/pdf/nfykdxxb-46-10-2456.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,"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":"Nan Fang Yi Ke Da Xue Xue Bao","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.98580002784729}],"study_designs":[{"display_name":"Observational Study","id":"https://openalex.org/study-designs/observational-study"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223797133.pdf","grobid_xml":"https://content.openalex.org/works/W7223797133.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4367323194","https://openalex.org/W4292868472","https://openalex.org/W4407285986","https://openalex.org/W4403968174","https://openalex.org/W4411549703","https://openalex.org/W4408572578","https://openalex.org/W4280491884","https://openalex.org/W4405630386","https://openalex.org/W4402225450","https://openalex.org/W2992579364"],"abstract_inverted_index":{"OBJECTIVES:":[0],"To":[1],"construct":[2,94],"a":[3,184],"prediction":[4,96,218,242],"model":[5,104,151,165,198,205],"for":[6,41,62,133,169,236,252],"occurrence":[7],"of":[8,18,20,43,49,102,107,110,187,195,202,227,234,243],"type":[9],"2":[10],"diabetic":[11],"retinopathy":[12],"(T2DR)":[13],"by":[14,99],"combining":[15,224],"the":[16,58,73,77,95,103,108,118,129,137,163,173,191,196,203,207,212,221,225,232],"principle":[17],"differentiation":[19],"syndrome":[21,50,74,124,228],"elements":[22,51,229],"with":[23,35,76],"machine":[24],"learning":[25],"(ML).":[26],"METHODS:":[27],"Cross-sectional":[28],"data":[29],"were":[30,91],"collected":[31],"from":[32],"273":[33],"patients":[34,40,247],"T2DR":[36,64,134,244],"and":[37,47,89,105,121,142,155,162,175,239],"376":[38],"T2DM":[39,246],"comparison":[42],"routine":[44],"test":[45],"results":[46],"parameters":[48],"in":[52,159,206,211,245],"traditional":[53],"Chinese":[54],"medicine":[55],"(TCM)":[56],"identify":[57],"candidate":[59],"TCM":[60,235],"indicators":[61],"predicting":[63,237],"occurrence.":[65,135],"LASSO":[66,115],"regression":[67],"analysis":[68,116],"was":[69,199],"used":[70,92],"to":[71,93,248],"select":[72],"features":[75,132,226],"highest":[78],"predictive":[79,112,131],"value.":[80],"Six":[81],"ML":[82,139,222],"algorithms":[83],"(LR,":[84],"ANN,":[85],"XGBoost,":[86],"RF,":[87],"SVM":[88],"KNN)":[90],"models,":[97,140],"followed":[98],"internal":[100],"validation":[101,213],"evaluation":[106],"importance":[109],"each":[111],"feature.":[113],"RESULTS:":[114],"identified":[117],"liver,":[119],"lung,":[120],"yang":[122],"excess":[123],"elements,":[125],"among":[126],"others,":[127],"as":[128],"most":[130],"Among":[136],"6":[138],"LR":[141,150,174,197],"KNN":[143,164,176,204],"models":[144,177,219],"showed":[145],"good":[146,167],"discrimination":[147],"power.":[148],"The":[149,216],"yielded":[152],"highly":[153],"reliable":[154],"robust":[156],"probability":[157,170],"output":[158],"global":[160],"calibration,":[161],"had":[166],"performance":[168],"predictions.":[171],"Both":[172],"provided":[178],"positive":[179],"net":[180,192],"clinical":[181,250],"benefits":[182],"over":[183],"wide":[185],"range":[186],"threshold":[188],"probabilities,":[189],"but":[190],"benefit":[193],"curve":[194],"above":[200],"that":[201],"whole":[208],"range,":[209],"especially":[210],"set.":[214],"CONCLUSIONS:":[215],"ML-based":[217],"(especially":[220],"model)":[223],"effectively":[230],"use":[231],"advantage":[233],"disease":[238],"allow":[240],"comprehensive":[241],"assist":[249],"decision-making":[251],"early":[253],"interventions.":[254]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-28T00:00:00"}