{"id":"https://openalex.org/W4367052841","doi":"https://doi.org/10.1177/02184923231171493","title":"Machine learning algorithms for population-specific risk score in coronary artery bypass grafting","display_name":"Machine learning algorithms for population-specific risk score in coronary artery bypass grafting","publication_year":2023,"publication_date":"2023-04-26","ids":{"openalex":"https://openalex.org/W4367052841","doi":"https://doi.org/10.1177/02184923231171493","pmid":"https://pubmed.ncbi.nlm.nih.gov/37122283"},"language":"en","primary_location":{"id":"doi:10.1177/02184923231171493","doi":"https://doi.org/10.1177/02184923231171493","is_oa":false,"landing_page_url":"https://doi.org/10.1177/02184923231171493","pdf_url":null,"source":{"id":"https://openalex.org/S207218074","display_name":"Asian Cardiovascular and Thoracic Annals","issn_l":"0218-4923","issn":["0218-4923","1816-5370"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","ki-jl-1","medline","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asian Cardiovascular and Thoracic Annals","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019403385","display_name":"Abhyuday Kumara Swamy","orcid":"https://orcid.org/0009-0002-3664-904X","observed_orcids":["https://orcid.org/0009-0002-3664-904X"]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhyuday Kumara Swamy","raw_affiliation_strings":["Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India","institution_ids":["https://openalex.org/I288496929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034471719","display_name":"Vivek Rajagopal","orcid":"https://orcid.org/0000-0001-9184-509X","observed_orcids":["https://orcid.org/0000-0001-9184-509X"]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vivek Rajagopal","raw_affiliation_strings":["Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0001-9184-509X","affiliations":[{"raw_affiliation_string":"Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India","institution_ids":["https://openalex.org/I288496929"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Deepak Krishnan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Krishnan","raw_affiliation_strings":["Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India","institution_ids":["https://openalex.org/I288496929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023782986","display_name":"Paramita Auddya Ghorai","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Paramita Auddya Ghorai","raw_affiliation_strings":["Department of Cardiothoracic Surgery, Narayana Health, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiothoracic Surgery, Narayana Health, India","institution_ids":["https://openalex.org/I288496929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029835937","display_name":"Santhosh Rathnam Palani","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Santhosh Rathnam Palani","raw_affiliation_strings":["Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Medha Analytics \u2013 Advanced analytics & AI, Narayana Health, Bengaluru, India","institution_ids":["https://openalex.org/I288496929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019659335","display_name":"Pradeep Narayan","orcid":"https://orcid.org/0000-0002-3843-1338","observed_orcids":["https://orcid.org/0000-0002-3843-1338","https://orcid.org/0009-0005-8984-4473"]},"institutions":[{"id":"https://openalex.org/I288496929","display_name":"Narayana Health","ror":"https://ror.org/05kx1ke03","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I288496929"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Pradeep Narayan","raw_affiliation_strings":["Department of Cardiothoracic Surgery, Narayana Health, India"],"raw_orcid":"https://orcid.org/0000-0002-3843-1338","affiliations":[{"raw_affiliation_string":"Department of Cardiothoracic Surgery, Narayana Health, India","institution_ids":["https://openalex.org/I288496929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5019659335"],"corresponding_institution_ids":["https://openalex.org/I288496929"],"apc_list":{"value":3500,"currency":"USD","value_usd":3500},"apc_paid":null,"fwci":1.2819,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77510939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"31","issue":"4","first_page":"348","last_page":"356"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11055","display_name":"Cardiac and Coronary Surgery Techniques","score":0.7437000274658203,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11055","display_name":"Cardiac and Coronary Surgery Techniques","score":0.7437000274658203,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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":0.19380000233650208,"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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.0142000000923872,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/coronary-artery-bypass-grafting","display_name":"coronary artery bypass grafting","score":0.9959999918937683},{"id":"https://openalex.org/keywords/preoperative-risk-prediction","display_name":"preoperative risk prediction","score":0.6980000138282776},{"id":"https://openalex.org/keywords/catboost","display_name":"CatBoost","score":0.902999997138977},{"id":"https://openalex.org/keywords/society-of-thoracic-surgeons-score","display_name":"Society of Thoracic Surgeons score","score":0.875},{"id":"https://openalex.org/keywords/euroscore-ii","display_name":"EuroSCORE II","score":0.9990000128746033},{"id":"https://openalex.org/keywords/indian-population","display_name":"Indian population","score":0.6150000095367432},{"id":"https://openalex.org/keywords/postoperative-mortality","display_name":"postoperative mortality","score":0.8690000176429749},{"id":"https://openalex.org/keywords/postoperative-morbidity","display_name":"postoperative morbidity","score":0.972000002861023},{"id":"https://openalex.org/keywords/prolonged-mechanical-ventilation","display_name":"prolonged mechanical ventilation","score":0.8730000257492065},{"id":"https://openalex.org/keywords/model-calibration","display_name":"model calibration","score":0.5149999856948853}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.9039667248725891},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.6878194212913513},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.6870698928833008},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.6823800802230835},{"id":"https://openalex.org/C207386681","wikidata":"https://www.wikidata.org/wiki/Q451323","display_name":"Derivation","level":3,"score":0.5954897999763489},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.5566900372505188},{"id":"https://openalex.org/C3017915907","wikidata":"https://www.wikidata.org/wiki/Q186020","display_name":"Bypass grafting","level":3,"score":0.4958823621273041},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4604419469833374},{"id":"https://openalex.org/C2779040412","wikidata":"https://www.wikidata.org/wiki/Q5411565","display_name":"EuroSCORE","level":3,"score":0.45238590240478516},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.43911072611808777},{"id":"https://openalex.org/C2776820930","wikidata":"https://www.wikidata.org/wiki/Q9655","display_name":"Artery","level":2,"score":0.349534273147583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2233516275882721},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001026","descriptor_name":"Coronary Artery Bypass","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":"D012307","descriptor_name":"Risk Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018570","descriptor_name":"Risk Assessment","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1177/02184923231171493","doi":"https://doi.org/10.1177/02184923231171493","is_oa":false,"landing_page_url":"https://doi.org/10.1177/02184923231171493","pdf_url":null,"source":{"id":"https://openalex.org/S207218074","display_name":"Asian Cardiovascular and Thoracic Annals","issn_l":"0218-4923","issn":["0218-4923","1816-5370"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","ki-jl-1","medline","poland-40","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asian Cardiovascular and Thoracic Annals","raw_type":"journal-article"},{"id":"pmid:37122283","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37122283","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":"Asian cardiovascular & thoracic annals","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":1.0}],"study_designs":[{"display_name":"Observational Study","id":"https://openalex.org/study-designs/observational-study"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1702536112","https://openalex.org/W1966716734","https://openalex.org/W1975627263","https://openalex.org/W2022202291","https://openalex.org/W2044355963","https://openalex.org/W2063507658","https://openalex.org/W2079582009","https://openalex.org/W2136335380","https://openalex.org/W2164290294","https://openalex.org/W2209325091","https://openalex.org/W2341500107","https://openalex.org/W2571536841","https://openalex.org/W2590062196","https://openalex.org/W2790553094","https://openalex.org/W2798476254","https://openalex.org/W3000714310","https://openalex.org/W3135894315","https://openalex.org/W3165870664","https://openalex.org/W4285045572"],"related_works":["https://openalex.org/W3165870664","https://openalex.org/W2147520846","https://openalex.org/W4402338038","https://openalex.org/W2432591132","https://openalex.org/W2018366473","https://openalex.org/W7138320247","https://openalex.org/W3217497768","https://openalex.org/W4313443984","https://openalex.org/W2104333697","https://openalex.org/W4416715081"],"abstract_inverted_index":{"Background":[0],"The":[1,45,82,124,199],"aim":[2],"of":[3,35,48,229,247],"this":[4],"study":[5],"was":[6,85,96,110,145,181,208,221],"to":[7,25,32,87,153,177,183,201,210,216,233],"develop":[8],"a":[9],"new":[10],"risk":[11,89,228,255],"prediction":[12],"score":[13,126,161,186,207,239,249],"(NH":[14,91],"Score)":[15],"for":[16,134,142,158,167,205,253,257,268],"patients":[17,50,258],"undergoing":[18,259],"coronary":[19],"artery":[20],"bypass":[21],"grafting":[22],"(CABG)":[23],"specific":[24],"the":[26,33,41,55,94,99,113,129,139,159,168,184,202,211,227,234,245],"Indian":[27,262],"population":[28],"and":[29,40,58,66,75,93,105,119,131,162,214,250],"compare":[30],"it":[31],"Society":[34],"Thoracic":[36],"Surgeon":[37],"(STS)":[38],"Score":[39,144,180,213,220],"EuroSCORE":[42,132,169,217,251],"II.":[43,218],"Method":[44],"baseline":[46],"features":[47],"adult":[49],"who":[51],"underwent":[52],"CABG":[53,260],"between":[54],"years":[56],"2015":[57],"2021":[59],"(":[60,171],"n":[61,72,79],"=":[62,73,80],"6703)":[63],"were":[64],"taken":[65],"split":[67],"into":[68],"training":[69],"data":[70,77],"(2015\u20132020;":[71],"5561)":[74],"validation":[76,100,267],"(2020\u20132021;":[78],"1142).":[81],"CatBoost":[83],"algorithm":[84],"trained":[86],"predict":[88],"scores":[90],"score),":[92],"performance":[95,246],"tested":[97],"on":[98],"set":[101],"by":[102,112],"Precision-Recall":[103],"Curve":[104],"F1":[106],"Score.":[107,236],"Model":[108],"calibration":[109],"measured":[111],"Brier":[114],"Score,":[115],"Expected":[116],"Calibration":[117,121],"Error":[118],"Maximum":[120],"Error.":[122],"Results":[123],"NH":[125,143,179,206,219,238],"outperformed":[127],"both":[128],"STS":[130,160,185,212,235,248],"II":[133,170,252],"all":[135],"outcomes.":[136],"For":[137],"mortality,":[138],"PR":[140],"AUC":[141],"(0.463":[146],"[95%":[147,155,164,188,193],"confidence":[148],"interval":[149],"[CI],":[150],"0.28\u20130.64])":[151],"compared":[152,232],"0.113":[154],"CI,":[156,165,189,194],"0.04\u20130.22]":[157],"0.146":[163],"0.06\u20130.31]":[166],"p":[172,196],"\u226a":[173],"0.0001).":[174,198],"With":[175],"respect":[176],"morbidity":[178],"superior":[182,209],"(0.43":[187],"0.33\u20130.50])":[190],"vs.":[191],"(0.229":[192],"0.18\u20130.3,":[195],"<":[197],"observed":[200],"predicted":[203],"ratio":[204],"similar":[215],"also":[222],"more":[223],"accurate":[224],"at":[225],"predicting":[226],"prolonged":[230],"ventilation":[231],"Conclusion":[237],"shows":[240],"an":[241],"excellent":[242],"improvement":[243],"over":[244],"modelling":[254],"predictions":[256],"in":[261],"population.":[263],"It":[264],"warrants":[265],"further":[266],"larger":[269],"datasets.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2023-04-27T00:00:00"}