{"id":"https://openalex.org/W7122656471","doi":"https://doi.org/10.3390/electronics15020312","title":"Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study","display_name":"Machine Learning-Based Blood Pressure Prediction Using Cardiovascular Disease Data: A Comprehensive Comparative Study","publication_year":2026,"publication_date":"2026-01-10","ids":{"openalex":"https://openalex.org/W7122656471","doi":"https://doi.org/10.3390/electronics15020312"},"language":"en","primary_location":{"id":"doi:10.3390/electronics15020312","doi":"https://doi.org/10.3390/electronics15020312","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15020312","pdf_url":"https://www.mdpi.com/2079-9292/15/2/312/pdf","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2079-9292/15/2/312/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006250449","display_name":"Irina Naskinova","orcid":"https://orcid.org/0009-0009-7794-4265","observed_orcids":["https://orcid.org/0009-0009-7794-4265"]},"institutions":[{"id":"https://openalex.org/I206451062","display_name":"University of Architecture, Civil Engineering and Geodesy","ror":"https://ror.org/033cz7157","country_code":"BG","type":"education","lineage":["https://openalex.org/I206451062"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Irina Naskinova","raw_affiliation_strings":["Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria"],"raw_orcid":"https://orcid.org/0009-0009-7794-4265","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria","institution_ids":["https://openalex.org/I206451062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102720679","display_name":"Mikhail Kolev","orcid":"https://orcid.org/0000-0001-7484-7106","observed_orcids":["https://orcid.org/0000-0001-7484-7106"]},"institutions":[{"id":"https://openalex.org/I206451062","display_name":"University of Architecture, Civil Engineering and Geodesy","ror":"https://ror.org/033cz7157","country_code":"BG","type":"education","lineage":["https://openalex.org/I206451062"]},{"id":"https://openalex.org/I47996466","display_name":"University of Warmia and Mazury in Olsztyn","ror":"https://ror.org/05s4feg49","country_code":"PL","type":"education","lineage":["https://openalex.org/I47996466"]}],"countries":["BG","PL"],"is_corresponding":true,"raw_author_name":"Mikhail Kolev","raw_affiliation_strings":["Department of Applied Computer Science and Mathematical Modelling, Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, Poland","Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Computer Science and Mathematical Modelling, Faculty of Mathematics and Computer Science, University of Warmia and Mazury, 10-710 Olsztyn, Poland","institution_ids":["https://openalex.org/I47996466"]},{"raw_affiliation_string":"Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria","institution_ids":["https://openalex.org/I206451062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119805157","display_name":"Dilyana Karova","orcid":"https://orcid.org/0009-0006-9577-654X","observed_orcids":["https://orcid.org/0009-0006-9577-654X"]},"institutions":[{"id":"https://openalex.org/I58918642","display_name":"Sofia University \"St. Kliment Ohridski\"","ror":"https://ror.org/02jv3k292","country_code":"BG","type":"education","lineage":["https://openalex.org/I58918642"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Dilyana Karova","raw_affiliation_strings":["Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University \u201cSt. Kliment Ohridski\u201d, 125 Tsarigradsko Shosse Blvd., bl. 3, 1113 Sofia, Bulgaria"],"raw_orcid":"https://orcid.org/0009-0006-9577-654X","affiliations":[{"raw_affiliation_string":"Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University \u201cSt. Kliment Ohridski\u201d, 125 Tsarigradsko Shosse Blvd., bl. 3, 1113 Sofia, Bulgaria","institution_ids":["https://openalex.org/I58918642"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082861082","display_name":"Mariyan Milev","orcid":"https://orcid.org/0000-0002-1756-3358","observed_orcids":["https://orcid.org/0000-0002-1756-3358"]},"institutions":[{"id":"https://openalex.org/I206451062","display_name":"University of Architecture, Civil Engineering and Geodesy","ror":"https://ror.org/033cz7157","country_code":"BG","type":"education","lineage":["https://openalex.org/I206451062"]},{"id":"https://openalex.org/I58918642","display_name":"Sofia University \"St. Kliment Ohridski\"","ror":"https://ror.org/02jv3k292","country_code":"BG","type":"education","lineage":["https://openalex.org/I58918642"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Mariyan Milev","raw_affiliation_strings":["Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria","Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University \u201cSt. Kliment Ohridski\u201d, 125 Tsarigradsko Shosse Blvd., bl. 3, 1113 Sofia, Bulgaria"],"raw_orcid":"https://orcid.org/0000-0002-1756-3358","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Architecture, Civil Engineering and Geodesy, 1 Hristo Smirnenski Blvd., 1164 Sofia, Bulgaria","institution_ids":["https://openalex.org/I206451062"]},{"raw_affiliation_string":"Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University \u201cSt. Kliment Ohridski\u201d, 125 Tsarigradsko Shosse Blvd., bl. 3, 1113 Sofia, Bulgaria","institution_ids":["https://openalex.org/I58918642"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102720679"],"corresponding_institution_ids":["https://openalex.org/I206451062","https://openalex.org/I47996466"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":28.869,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.99574784,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"15","issue":"2","first_page":"312","last_page":"312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9057999849319458,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9057999849319458,"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/T10144","display_name":"Blood Pressure and Hypertension Studies","score":0.09030000120401382,"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"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/blood-pressure-prediction","display_name":"blood pressure prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/hypertension","display_name":"hypertension","score":0.7200000286102295},{"id":"https://openalex.org/keywords/cardiovascular-disease","display_name":"cardiovascular disease","score":0.6570000052452087},{"id":"https://openalex.org/keywords/catboost","display_name":"CatBoost","score":0.7519999742507935},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.8820000290870667},{"id":"https://openalex.org/keywords/systolic-blood-pressure","display_name":"systolic blood pressure","score":0.8379999995231628},{"id":"https://openalex.org/keywords/diastolic-blood-pressure","display_name":"diastolic blood pressure","score":0.996999979019165},{"id":"https://openalex.org/keywords/cardiovascular-risk-factors","display_name":"cardiovascular risk factors","score":0.9599999785423279},{"id":"https://openalex.org/keywords/anthropometry","display_name":"anthropometry","score":0.9810000061988831},{"id":"https://openalex.org/keywords/demographic-data","display_name":"demographic data","score":0.5709999799728394},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"feature engineering","score":0.7710000276565552},{"id":"https://openalex.org/keywords/forecasting-model-comparison","display_name":"forecasting model comparison","score":0.5249999761581421}],"concepts":[{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.794700026512146},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.666100025177002},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5720999836921692},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C61427482","wikidata":"https://www.wikidata.org/wiki/Q6656244","display_name":"Anthropometry","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41260001063346863},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C107130276","wikidata":"https://www.wikidata.org/wiki/Q133805","display_name":"Epidemiology","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.38749998807907104},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C50440223","wikidata":"https://www.wikidata.org/wiki/Q1475848","display_name":"Risk factor","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.3788999915122986},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.36899998784065247},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C138528620","wikidata":"https://www.wikidata.org/wiki/Q2510752","display_name":"Explained variation","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C57900726","wikidata":"https://www.wikidata.org/wiki/Q492905","display_name":"Diastole","level":3,"score":0.32170000672340393},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3061000108718872},{"id":"https://openalex.org/C3018284874","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Cardiovascular health","level":3,"score":0.2912999987602234},{"id":"https://openalex.org/C178853913","wikidata":"https://www.wikidata.org/wiki/Q1642137","display_name":"Hemodynamics","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26910001039505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C138816342","wikidata":"https://www.wikidata.org/wiki/Q189603","display_name":"Public health","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/electronics15020312","doi":"https://doi.org/10.3390/electronics15020312","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15020312","pdf_url":"https://www.mdpi.com/2079-9292/15/2/312/pdf","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/electronics15020312","doi":"https://doi.org/10.3390/electronics15020312","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15020312","pdf_url":"https://www.mdpi.com/2079-9292/15/2/312/pdf","source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7122656471.pdf","grobid_xml":"https://content.openalex.org/works/W7122656471.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W75245760","https://openalex.org/W197059223","https://openalex.org/W1537066827","https://openalex.org/W2123835710","https://openalex.org/W2144360877","https://openalex.org/W2295598076","https://openalex.org/W2395172628","https://openalex.org/W2431637923","https://openalex.org/W2572331255","https://openalex.org/W2625625371","https://openalex.org/W2884013251","https://openalex.org/W2920258695","https://openalex.org/W2949676527","https://openalex.org/W2973761055","https://openalex.org/W2982542510","https://openalex.org/W2989335918","https://openalex.org/W3012343632","https://openalex.org/W3102947452","https://openalex.org/W3171925877","https://openalex.org/W4220845336","https://openalex.org/W4226113147","https://openalex.org/W4283320106","https://openalex.org/W4313478509","https://openalex.org/W4328125291","https://openalex.org/W4388212341","https://openalex.org/W4392983253","https://openalex.org/W4399180438","https://openalex.org/W4401797763","https://openalex.org/W4401924418","https://openalex.org/W4402378922","https://openalex.org/W4414888981","https://openalex.org/W4415094257"],"related_works":["https://openalex.org/W4382400703","https://openalex.org/W7216967355","https://openalex.org/W6893358324","https://openalex.org/W7208808288","https://openalex.org/W4316658727","https://openalex.org/W4392253949","https://openalex.org/W7124694722","https://openalex.org/W4406415616","https://openalex.org/W6995845632","https://openalex.org/W7134938488"],"abstract_inverted_index":{"Hypertension":[0],"remains":[1],"one":[2,14],"of":[3,37,98,163,168,171,180,185,240],"the":[4,40,109,205,257],"most":[5],"pressing":[6],"public":[7],"health":[8],"challenges":[9],"worldwide,":[10],"affecting":[11],"more":[12],"than":[13],"billion":[15],"individuals":[16,93],"and":[17,27,58,84,126,166,183,208],"serving":[18],"as":[19],"a":[20,34,157,194,219],"principal":[21],"risk":[22,78,124],"factor":[23],"for":[24,237,247,259],"cardiovascular":[25,77,105],"morbidity":[26],"mortality.":[28],"Whilst":[29],"blood":[30,44,73,153,175,197,223,241],"pressure":[31,45,74,154,176,198,224,242],"measurement":[32],"constitutes":[33],"routine":[35],"component":[36],"clinical":[38,123,209,229],"practice,":[39],"capacity":[41],"to":[42,142,202,266],"predict":[43],"values":[46],"from":[47,91,103,137,204,226],"readily":[48],"obtainable":[49],"patient":[50,100],"characteristics":[51],"could":[52],"substantially":[53],"enhance":[54],"preventive":[55],"care":[56],"strategies":[57],"facilitate":[59],"timely":[60],"intervention.":[61],"The":[62,231],"present":[63],"study":[64],"examines":[65],"whether":[66],"machine":[67,248],"learning":[68,249],"methodologies":[69],"can":[70],"reliably":[71],"forecast":[72],"measurements":[75],"utilizing":[76],"factors":[79],"in":[80,213],"conjunction":[81],"with":[82,156,178],"demographic":[83,118],"anthropometric":[85],"data.":[86,230],"We":[87],"have":[88],"analyzed":[89],"data":[90,261],"68,616":[92],"following":[94],"rigorous":[95],"quality":[96],"assessment":[97],"70,000":[99],"records":[101],"obtained":[102],"Kaggle\u2019s":[104],"disease":[106],"repository.":[107],"Beyond":[108],"10":[110],"original":[111],"variables,":[112],"we":[113],"engineered":[114],"additional":[115],"features":[116],"encompassing":[117],"patterns,":[119],"body":[120],"composition":[121],"indices,":[122],"indicators,":[125],"their":[127],"interactions.":[128],"Nine":[129],"distinct":[130],"predictive":[131,270],"models":[132],"were":[133],"systematically":[134],"evaluated,":[135],"spanning":[136],"elementary":[138],"baseline":[139],"approaches":[140],"through":[141],"sophisticated":[143],"gradient":[144],"boosting":[145],"ensembles.":[146],"CatBoost":[147],"demonstrated":[148],"superior":[149],"performance,":[150],"yielding":[151],"systolic":[152],"predictions":[155,177],"root":[158],"mean":[159],"squared":[160],"error":[161],"(RMSE)":[162],"14.37":[164],"mmHg":[165,182],"coefficient":[167],"determination":[169],"(R2)":[170],"0.265,":[172],"alongside":[173],"diastolic":[174],"RMSE":[179],"8.57":[181],"R2":[184],"0.187.":[186],"These":[187,216],"modest":[188],"explained":[189],"variance":[190,243],"values\u2014substantially":[191],"below":[192],"unity\u2014reveal":[193],"fundamental":[195],"limitation:":[196],"proves":[199],"remarkably":[200],"resistant":[201],"prediction":[203,225],"demographic,":[206],"anthropometric,":[207],"variables":[210,235],"typically":[211],"available":[212],"epidemiological":[214],"datasets.":[215],"findings":[217],"illuminate":[218],"sobering":[220],"reality":[221],"regarding":[222],"routinely":[227],"collected":[228],"observation":[232],"that":[233],"standard":[234],"account":[236],"merely":[238],"one-quarter":[239],"should":[244],"temper":[245],"expectations":[246],"applications":[250],"within":[251],"this":[252],"domain,":[253],"whilst":[254],"simultaneously":[255],"underscoring":[256],"necessity":[258],"richer":[260],"sources":[262],"or":[263],"novel":[264],"biomarkers":[265],"achieve":[267],"clinically":[268],"meaningful":[269],"accuracy.":[271]},"counts_by_year":[{"year":2026,"cited_by_count":8}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-01-13T00:00:00"}
