{"id":"https://openalex.org/W4410328506","doi":"https://doi.org/10.1186/s13040-025-00450-z","title":"Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records","display_name":"Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records","publication_year":2025,"publication_date":"2025-05-13","ids":{"openalex":"https://openalex.org/W4410328506","doi":"https://doi.org/10.1186/s13040-025-00450-z","pmid":"https://pubmed.ncbi.nlm.nih.gov/40361167"},"language":"en","primary_location":{"id":"doi:10.1186/s13040-025-00450-z","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00450-z","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00450-z","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00450-z","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065766791","display_name":"Berit Hunsdieck","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]},{"id":"https://openalex.org/I67348948","display_name":"Bayer (Germany)","ror":"https://ror.org/04hmn8g73","country_code":"DE","type":"company","lineage":["https://openalex.org/I67348948"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Berit Hunsdieck","raw_affiliation_strings":["Computational Biology, Bayer AG, Wuppertal, Germany. berit.hunsdieck@bayer.com","Department of Statistics, TU Dortmund University, Dortmund, Germany. berit.hunsdieck@bayer.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Biology, Bayer AG, Wuppertal, Germany. berit.hunsdieck@bayer.com","institution_ids":["https://openalex.org/I67348948"]},{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany. berit.hunsdieck@bayer.com","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058699275","display_name":"Christian Bender","orcid":"https://orcid.org/0000-0003-2630-1453"},"institutions":[{"id":"https://openalex.org/I67348948","display_name":"Bayer (Germany)","ror":"https://ror.org/04hmn8g73","country_code":"DE","type":"company","lineage":["https://openalex.org/I67348948"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Bender","raw_affiliation_strings":["Computational Biology, Bayer AG, Wuppertal, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Biology, Bayer AG, Wuppertal, Germany","institution_ids":["https://openalex.org/I67348948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063815245","display_name":"Katja Ickstadt","orcid":"https://orcid.org/0000-0001-5157-2496"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]},{"id":"https://openalex.org/I283854653","display_name":"Machine Intelligence Research Institute","ror":"https://ror.org/01h4ass90","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I283854653"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Katja Ickstadt","raw_affiliation_strings":["Department of Statistics, TU Dortmund University, Dortmund, Germany","Lamarr-Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"Lamarr-Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","institution_ids":["https://openalex.org/I283854653"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082020092","display_name":"Johanna Mielke","orcid":"https://orcid.org/0000-0002-4239-5499"},"institutions":[{"id":"https://openalex.org/I67348948","display_name":"Bayer (Germany)","ror":"https://ror.org/04hmn8g73","country_code":"DE","type":"company","lineage":["https://openalex.org/I67348948"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Johanna Mielke","raw_affiliation_strings":["Computational Biology, Bayer AG, Wuppertal, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Biology, Bayer AG, Wuppertal, Germany","institution_ids":["https://openalex.org/I67348948"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1690,"currency":"GBP","value_usd":2072},"apc_paid":{"value":1690,"currency":"GBP","value_usd":2072},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04555246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"1","first_page":"35","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.41530001163482666,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.41530001163482666,"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"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.09109999984502792,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.08869999647140503,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7311112880706787},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.5336689949035645},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4773041009902954},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46804380416870117},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.4518038332462311},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42392608523368835},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4159941077232361},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.4109506607055664},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3617324233055115},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.31043994426727295},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18350830674171448},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17181140184402466},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1631755530834198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311112880706787},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.5336689949035645},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4773041009902954},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46804380416870117},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4518038332462311},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42392608523368835},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4159941077232361},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.4109506607055664},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3617324233055115},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.31043994426727295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18350830674171448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17181140184402466},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1631755530834198},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1186/s13040-025-00450-z","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00450-z","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00450-z","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},{"id":"pmid:40361167","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40361167","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,"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":"BioData mining","raw_type":null},{"id":"pmh:oai:doaj.org/article:ef4e3eaeee1240d3849effb63712e906","is_oa":true,"landing_page_url":"https://doaj.org/article/ef4e3eaeee1240d3849effb63712e906","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,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Mining, Vol 18, Iss 1, Pp 1-27 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12070788","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12070788","pdf_url":null,"source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BioData Min","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s13040-025-00450-z","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13040-025-00450-z","pdf_url":"https://biodatamining.biomedcentral.com/counter/pdf/10.1186/s13040-025-00450-z","source":{"id":"https://openalex.org/S84409260","display_name":"BioData Mining","issn_l":"1756-0381","issn":["1756-0381"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BioData Mining","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410328506.pdf","grobid_xml":"https://content.openalex.org/works/W4410328506.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W115293911","https://openalex.org/W1007659581","https://openalex.org/W1565283841","https://openalex.org/W1570622790","https://openalex.org/W1823654834","https://openalex.org/W1951724000","https://openalex.org/W1951842251","https://openalex.org/W1969574651","https://openalex.org/W1990540788","https://openalex.org/W1992054897","https://openalex.org/W2032758468","https://openalex.org/W2055290874","https://openalex.org/W2082704080","https://openalex.org/W2094114209","https://openalex.org/W2141861294","https://openalex.org/W2152849583","https://openalex.org/W2155965977","https://openalex.org/W2160616692","https://openalex.org/W2284497347","https://openalex.org/W2338423082","https://openalex.org/W2525237094","https://openalex.org/W2559950083","https://openalex.org/W2607031541","https://openalex.org/W2750267139","https://openalex.org/W2963208580","https://openalex.org/W3177547797","https://openalex.org/W3215318564","https://openalex.org/W4311691554","https://openalex.org/W4385479588","https://openalex.org/W4408460145"],"related_works":["https://openalex.org/W4237277701","https://openalex.org/W2121424666","https://openalex.org/W1587028174","https://openalex.org/W4301250310","https://openalex.org/W2030928163","https://openalex.org/W2067700950","https://openalex.org/W1669998927","https://openalex.org/W2182768446","https://openalex.org/W2620237082","https://openalex.org/W2055420561"],"abstract_inverted_index":{"BACKGROUND:":[0],"Over":[1],"the":[2,47,81,123,162,166,178,183,190,201,204],"past":[3],"decade":[4],"an":[5,99],"increase":[6],"in":[7,171],"usage":[8],"of":[9,72,84,110,173,182,192,203],"electronic":[10],"health":[11],"data":[12,24,32,87,111],"(EHR)":[13],"by":[14,103],"office-based":[15],"physicians":[16],"and":[17,31,34,105,117,126,157,180,200],"hospitals":[18],"has":[19],"been":[20],"reported.":[21],"However,":[22],"these":[23,44],"types":[25],"come":[26],"with":[27,62,185],"challenge":[28],"regarding":[29],"completeness":[30],"quality":[33,83],"it":[35],"is,":[36],"especially":[37],"for":[38,80],"more":[39],"complex":[40],"models,":[41],"unclear":[42],"how":[43],"characteristics":[45,109],"influence":[46,191,202],"performance.":[48,175],"METHODS:":[49],"In":[50],"this":[51,73],"paper,":[52],"we":[53],"focus":[54],"on":[55,195,209],"joint":[56,90,124,163],"models":[57,91,125],"which":[58],"combines":[59],"longitudinal":[60,85],"modelling":[61,64,135],"survival":[63,134],"to":[65,76,131],"incorporate":[66],"all":[67],"available":[68],"information.":[69],"The":[70],"aim":[71],"paper":[74],"is":[75],"establish":[77],"simulation-based":[78],"guidelines":[79,184],"necessary":[82],"EHR":[86],"so":[88],"that":[89,141],"perform":[92],"better":[93],"than":[94],"cox":[95],"models.":[96],"We":[97,121,176],"conducted":[98],"extensive":[100],"simulation":[101],"study":[102],"systematically":[104],"transparently":[106],"varying":[107],"different":[108],"quality,":[112],"e.g.,":[113],"measurement":[114,160],"frequency,":[115],"noise,":[116],"heterogeneity":[118],"between":[119],"patients.":[120],"apply":[122],"evaluate":[127],"their":[128],"performance":[129],"relative":[130],"traditional":[132,167],"Cox":[133,168],"techniques.":[136],"RESULTS:":[137],"Key":[138],"findings":[139],"suggest":[140],"biomarker":[142],"changes":[143],"before":[144],"disease":[145],"onset":[146],"must":[147],"be":[148],"consistent":[149],"within":[150],"similar":[151],"patient":[152],"groups.":[153],"With":[154],"increasing":[155],"noise":[156],"a":[158],"higher":[159],"density,":[161],"model":[164,170,174],"surpasses":[165],"regression":[169],"terms":[172],"illustrate":[177],"usefulness":[179],"limitations":[181],"two":[186],"real-world":[187],"examples,":[188],"namely":[189],"serum":[193],"bilirubin":[194],"primary":[196],"biliary":[197],"liver":[198],"cirrhosis":[199],"estimated":[205],"glomerular":[206],"filtration":[207],"rate":[208],"chronic":[210],"kidney":[211],"disease.":[212]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
