{"id":"https://openalex.org/W7225758744","doi":"https://doi.org/10.3389/frai.2026.1986141","title":"Machine learning prediction of spirometry-defined airflow obstruction in older Indian adults: a multimodel benchmark, simplified bedside score, and temporal holdout validation","display_name":"Machine learning prediction of spirometry-defined airflow obstruction in older Indian adults: a multimodel benchmark, simplified bedside score, and temporal holdout validation","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225758744","doi":"https://doi.org/10.3389/frai.2026.1986141"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2026.1986141","doi":"https://doi.org/10.3389/frai.2026.1986141","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1986141","pdf_url":null,"source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3389/frai.2026.1986141","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031753201","display_name":"Rupeshkumar Deshmukh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I887998513","display_name":"Bharati Vidyapeeth (Deemed to be University)","ror":"https://ror.org/0052mmx10","country_code":"IN","type":"education","lineage":["https://openalex.org/I887998513"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Rupeshkumar Deshmukh","raw_affiliation_strings":["Department of Community Medicine, Bharati Vidyapeeth (Deemed to be University) Medical College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Community Medicine, Bharati Vidyapeeth (Deemed to be University) Medical College","institution_ids":["https://openalex.org/I887998513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5153626411","display_name":"Sudhanshu Mahajan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I887998513","display_name":"Bharati Vidyapeeth (Deemed to be University)","ror":"https://ror.org/0052mmx10","country_code":"IN","type":"education","lineage":["https://openalex.org/I887998513"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudhanshu Mahajan","raw_affiliation_strings":["Department of Community Medicine, Bharati Vidyapeeth (Deemed to be University) Medical College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Community Medicine, Bharati Vidyapeeth (Deemed to be University) Medical College","institution_ids":["https://openalex.org/I887998513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5031753201"],"corresponding_institution_ids":["https://openalex.org/I887998513"],"apc_list":{"value":1950,"currency":"CHF","value_usd":2353},"apc_paid":{"value":1950,"currency":"CHF","value_usd":2353},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10143","display_name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T10143","display_name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T14374","display_name":"Statistical Methods in Epidemiology","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.00019999999494757503,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5842000246047974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.553600013256073},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5468000173568726},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5302000045776367},{"id":"https://openalex.org/C116067010","wikidata":"https://www.wikidata.org/wiki/Q4698686","display_name":"Airflow","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C2780333948","wikidata":"https://www.wikidata.org/wiki/Q653305","display_name":"Spirometry","level":3,"score":0.4480000138282776},{"id":"https://openalex.org/C2776135927","wikidata":"https://www.wikidata.org/wiki/Q5242559","display_name":"Referral","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33730000257492065},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3389/frai.2026.1986141","doi":"https://doi.org/10.3389/frai.2026.1986141","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1986141","pdf_url":null,"source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3389/frai.2026.1986141","doi":"https://doi.org/10.3389/frai.2026.1986141","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1986141","pdf_url":null,"source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1994682257","https://openalex.org/W2045030989","https://openalex.org/W2099449365","https://openalex.org/W2125078269","https://openalex.org/W2141007997","https://openalex.org/W2146758572","https://openalex.org/W2163267243","https://openalex.org/W2254088306","https://openalex.org/W2417116130","https://openalex.org/W2545337398","https://openalex.org/W2889606340","https://openalex.org/W2898753907","https://openalex.org/W2913997948","https://openalex.org/W2977262858","https://openalex.org/W2981869278","https://openalex.org/W2996480032","https://openalex.org/W2999309192","https://openalex.org/W3012413426","https://openalex.org/W3209740252","https://openalex.org/W4206246326","https://openalex.org/W4229443985","https://openalex.org/W4233026002","https://openalex.org/W4281717669","https://openalex.org/W4322758476","https://openalex.org/W4388419725","https://openalex.org/W4394857110","https://openalex.org/W4404217594","https://openalex.org/W4408294397","https://openalex.org/W4413824512","https://openalex.org/W4414668230","https://openalex.org/W7128210471"],"related_works":[],"abstract_inverted_index":{"Background":[0],"Chronic":[1],"obstructive":[2],"pulmonary":[3],"disease":[4,291],"(COPD)":[5],"is":[6,20,103,679,716],"substantially":[7],"underdiagnosed":[8],"in":[9,15,220,231,236,268,444,448,637,682],"India.":[10],"The":[11,194,215,667],"Longitudinal":[12],"Ageing":[13],"Study":[14],"India":[16],"(LASI)":[17],"Wave":[18,64],"1":[19,65],"the":[21,87,101,126,132,143,225,232,297,301,306,331,344,375,411,433,451,456,466,496,501,544,571,574,580,590,601,604,631,638,674,728],"first":[22],"nationally":[23],"representative":[24],"survey":[25,741],"to":[26,31,150,319,363,503,539,556,583,603,618,623,692,696,727,733,738],"administer":[27],"objective,":[28],"pre-bronchodilator":[29],"spirometry":[30,533,698,713],"older":[32],"Indian":[33],"adults.":[34],"We":[35,60,170],"examined":[36,235],"how":[37],"well":[38],"routinely":[39],"collected,":[40],"non-spirometric":[41],"information":[42,647],"identifies":[43],"spirometry-defined":[44,104,649],"airflow":[45,105,650],"obstruction,":[46],"and":[47,107,129,180,184,187,208,224,253,309,321,334,372,405,420,423,432,439,446,469,479,493,518,579,586,654,678,684,731,737],"whether":[48],"a":[49,109,118,157,188,209,242,254,288,548,565,663,708,722,734,739],"simple":[50],"bedside":[51,380,591,668],"score":[52,381,592,669],"could":[53,705],"help":[54],"prioritise":[55],"referral":[56,528],"for":[57,191,305,311,326,330,336,465,472,488,532,597,711],"spirometry.":[58],"Methods":[59],"analysed":[61,213],"37,754":[62],"LASI":[63],"respondents":[66],"aged":[67],"\u226545":[68],"years":[69],"with":[70,135,257,338,350,474,558,573,676],"usable":[71],"spirometry,":[72],"of":[73,112,125,163,228,270,282,402,414,476,481,526,537,543,551,634,673],"whom":[74],"35,051":[75,271],"had":[76,285,399,593],"complete":[77,195],"data":[78,145,233],"on":[79,123,131,142,296,322,407,455,490],"26":[80],"predictors.":[81,377],"Airflow":[82,264],"obstruction":[83,106,265,651,677,694],"was":[84,98,166,198,212,218,266,294,303,418,437,442,460,485,569,577],"defined":[85],"by":[86,139,201,205],"GOLD":[88],"fixed-ratio":[89,729],"criterion":[90],"(pre-bronchodilator":[91],"FEV1/FVC":[92],"<":[93,388],"0.70);":[94],"because":[95],"post-bronchodilator":[96],"testing":[97],"not":[99,108,509,689,732],"performed,":[100],"outcome":[102,211],"clinical":[110,700],"diagnosis":[111],"COPD.":[113],"Ten":[114],"prediction":[115],"approaches,":[116],"including":[117],"stacked":[119,307,332,467],"ensemble,":[120],"were":[121,234,374,430,609],"developed":[122],"80%":[124],"2017\u201318":[127],"interviews":[128],"tested":[130],"remaining":[133],"20%,":[134],"classification":[136],"thresholds":[137],"chosen":[138],"10-fold":[140],"cross-validation":[141],"training":[144],"only.":[146],"Interviews":[147],"from":[148,168,361,561],"2019":[149],"2021":[151],"(":[152],"n":[153],"=":[154],"3,023)":[155],"formed":[156],"temporal":[158,323,408,491,620],"holdout":[159],"sample,":[160],"one":[161],"state":[162],"which":[164],"(Sikkim)":[165],"absent":[167],"development.":[169],"report":[171],"bootstrap":[172,190],"confidence":[173],"intervals,":[174],"paired":[175],"AUC":[176,302,356,401,550,581],"differences,":[177],"calibration":[178],"intercept":[179],"slope,":[181],"Brier":[182],"scores,":[183],"net":[185],"benefit,":[186],"PSU-cluster":[189],"survey-weighted":[192,274],"prevalence.":[193],"modelling":[196],"procedure":[197,546],"also":[199],"evaluated":[200],"nested":[202],"resampling":[203,542],"grouped":[204],"sampling":[206],"unit,":[207],"lower-limit-of-normal":[210,243,566,735],"separately.":[214],"principal":[216],"analysis":[217,521,602],"repeated":[219],"examinations":[221,608],"flagged":[222,610],"acceptable,":[223],"lower":[226,632],"limits":[227],"normal":[229,635],"supplied":[230,636],"healthy":[237,626],"never-smokers.":[238],"Sensitivity":[239,441],"analyses":[240],"covered":[241],"outcome,":[244],"class-imbalance":[245],"correction,":[246],"missing":[247],"data,":[248],"inverse-probability":[249],"weighting,":[250],"laboratory-free":[251],"models,":[252],"logistic":[255,348,665],"model":[256,308,468,562],"spline":[258,351],"terms.":[259],"Reporting":[260],"followed":[261],"TRIPOD+AI.":[262],"Results":[263],"present":[267],"4,212":[269],"participants":[272,284,606],"(12.0%;":[273],"prevalence":[275,359,568],"12.6%,":[276],"95%":[277,316],"CI":[278,317,463,554],"11.8\u201313.5%);":[279],"only":[280,652],"10.9%":[281],"these":[283],"ever":[286,393],"reported":[287],"chronic":[289],"lung":[290],"diagnosis.":[292],"Discrimination":[293],"modest:":[295],"internal":[298,457],"test":[299,458,616],"set":[300,459],"0.686":[304],"0.685":[310],"CatBoost":[312,489],"(paired":[313],"difference":[314],"0.001,":[315],"\u22120.002":[318],"0.004),":[320],"validation":[324,621],"0.674":[325],"random":[327],"forest,":[328],"0.665":[329],"model,":[333],"0.662":[335],"CatBoost,":[337,473],"no":[339,523,559,594,659],"statistically":[340],"supported":[341],"differences":[342],"between":[343],"leading":[345,376],"models.":[346],"A":[347,378],"regression":[349],"terms":[352],"performed":[353],"similarly":[354],"(internal":[355,615],"0.683).":[357],"Obstruction":[358],"ranged":[360],"1.8":[362],"25.1%":[364],"across":[365],"states.":[366],"Age,":[367],"BMI,":[368],"state,":[369],"tobacco":[370,394],"use,":[371,395],"sex":[373,587],"five-item":[379],"(age":[382,585],"\u226565":[383],"years,":[384],"male":[385],"sex,":[386],"BMI":[387],"21":[389],"kg/m":[390],"2":[391],",":[392],"waist":[396],"\u226590":[397],"cm)":[398],"an":[400],"0.633":[403],"internally":[404],"0.621":[406],"validation;":[409],"at":[410,534],"cross-validated":[412,452],"threshold":[413,498,535],"\u22655":[415],"points,":[416],"sensitivity":[417,475],"54.0":[419],"45.5%,":[421],"5.7":[422],"4.2":[424],"cases":[425],"per":[426],"100":[427],"people":[428,675],"screened":[429],"missed,":[431],"positive":[434],"predictive":[435],"value":[436],"18.4":[438],"11.3%.":[440],"73.5%":[443],"men":[445],"21.1%":[447],"women.":[449],"At":[450,702],"thresholds,":[453],"accuracy":[454,484],"0.637":[461],"(95%":[462,553],"0.626\u20130.649)":[464],"0.657":[470],"(0.646\u20130.667)":[471],"about":[477,482,516],"60%":[478],"specificity":[480],"65%;":[483],"0.749":[486],"(0.731\u20130.762)":[487],"validation,":[492],"it":[494,687,704],"reflected":[495],"operating":[497],"more":[499],"than":[500],"ability":[502],"rank":[504],"participants.":[505],"Class":[506],"weighting":[507],"did":[508],"improve":[510],"discrimination":[511,596],"but":[512],"inflated":[513],"predicted":[514],"risks":[515],"threefold,":[517],"decision":[519],"curve":[520],"showed":[522,658],"consistent":[524],"advantage":[525,661],"risk-based":[527],"over":[529,662],"referring":[530],"everyone":[531],"probabilities":[536],"5":[538],"10%.":[540],"Nested":[541],"whole":[545],"gave":[547,612],"pooled":[549],"0.687":[552],"0.677":[555],"0.697),":[557],"optimism":[560],"selection.":[563],"With":[564],"definition,":[567,736],"14.1%,":[570],"kappa":[572],"fixed":[575],"ratio":[576],"0.81,":[578],"fell":[582,629],"0.61":[584],"alone:":[588],"0.52);":[589],"useful":[595],"that":[598],"outcome.":[599],"Restricting":[600],"28,805":[605],"whose":[607],"acceptable":[611],"similar":[613],"AUCs":[614],"0.679":[617],"0.689;":[619],"0.646":[622],"0.671).":[624],"Among":[625],"never-smokers,":[627],"11.5%":[628],"below":[630],"limit":[633],"file,":[639],"against":[640,721],"5%":[641],"expected.":[642],"Conclusion":[643],"Routinely":[644],"collected":[645],"non-invasive":[646],"discriminates":[648],"modestly,":[653],"complex":[655],"machine-learning":[656],"models":[657],"demonstrable":[660],"well-specified":[664],"model.":[666],"misses":[670],"roughly":[671],"half":[672],"least":[680],"sensitive":[681],"women":[683],"younger":[685],"never-smokers;":[686],"should":[688],"be":[690],"used":[691],"exclude":[693],"or":[695,699],"replace":[697],"judgement.":[701],"most,":[703],"serve":[706],"as":[707],"preliminary":[709],"aid":[710],"prioritising":[712],"where":[714],"capacity":[715],"limited,":[717],"pending":[718],"prospective":[719],"evaluation":[720],"symptom-based":[723],"pathway.":[724],"Findings":[725],"apply":[726],"definition":[730],"single":[740],"wave.":[742]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-11T00:00:00"}