https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/Head https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/assertion https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/provenance https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/pubinfo https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/assertion https://ieeexplore.ieee.org/document/10564463 http://purl.org/dc/terms/creator https://orcid.org/0009-0001-1115-9741 https://ieeexplore.ieee.org/document/10564463 http://purl.org/dc/terms/publisher https://ror.org/01n002310 https://ieeexplore.ieee.org/document/10564463 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://ieeexplore.ieee.org/document/10564463 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://ieeexplore.ieee.org/document/10564463 http://www.w3.org/2000/01/rdf-schema#comment This research investigates the impact of missing data on the performance of machine learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to investigate the extent to which missing data negatively impacts the training of machine learning algorithms, and whether demographic groups with a higher proportion of missing data (i.e.,ethnicity) have lower predictive accuracy. Using advanced machine learning and data analysis techniques, our results highlight important considerations related to missing data in medical datasets and provide useful insights for improving predictive modeling and decision support systems in clinical practice offers. Major findings:This investigation leveraged the MIMIC-IV v2.2 dataset—containing de-identified data from 73,141 ICU admissions at Beth Israel Deaconess Medical Center—to study the impact of missing data on machine learning. The research found that while electronic health records (EHRs) offer massive clinical datasets, they are often non-standardized and riddled with missing values. By predicting hospital Length of Stay (LOS), the study showed that as data is missing "not at random," algorithm performance (measured by RMSE) degrades. Specifically, when datasets were intentionally biased to have more missing entries for certain racial groups (Asian, Black, Hispanic, etc.), the predictive error for those specific groups increased in 83% of "aggressive" data removal tests. This highlights that simply imputing or completing missing data can entrench existing healthcare inequities. https://ieeexplore.ieee.org/document/10564463 http://www.w3.org/2000/01/rdf-schema#label Addressing the Challenge of Missing Medical Data in Healthcare Analytics: A Focus on Machine Learning Predictions for ICU Length of Stay https://ieeexplore.ieee.org/document/10564463 https://schema.org/funder https://ror.org/04fs6r254 https://ieeexplore.ieee.org/document/10564463 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://ieeexplore.ieee.org/document/10564463 https://www.w3.org/ns/dcat#contactPoint mahmad.isaq@outlook.com https://ieeexplore.ieee.org/document/10564463 https://www.w3.org/ns/dcat#endDate 2024 https://ieeexplore.ieee.org/document/10564463 https://www.w3.org/ns/dcat#startDate 2023 https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/provenance https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://purl.org/dc/terms/created 2026-01-14T05:53:32.919Z https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/document/10564463 https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/sig http://purl.org/nanopub/x/hasSignature ew01BbW15Sne2LHMMUh5OFGNSNoEiFOf0YvAGWbToB5SlqhFDy27lOOt4ByCI2tJ2ElqcYWl+GfHFJt0FFR7ET5ZU65hxRrqNjVqz1hMXZhcqdHpEgJYOxKUoOHz4QfRs6uvreJlW33Pol9XIAxJ89jCJuhUVP2HbkF9e83wK/39Q9OAFi1kOqI3iPIXlM3ZOopFBF4/yy56kbesc7MZIKN6uMBry6913XNy3PpLkMDGpwCh2dfFA3osGCuG1qQdYHaIFTfW9o+55xDtxM0yt8WCZMYhdobhYlb0NPya2Gh5TwoQ0BKGX6c4SquQyPXXIqQ2L7SsMXNrZsarPqiH7w== https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742