https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/Head https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/assertion https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/provenance https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/pubinfo https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/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/021nxhr62 https://ieeexplore.ieee.org/document/10564463 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c 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/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/provenance https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/dc/terms/created 2026-01-21T17:46:40.759Z https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/document/10564463 https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/nanopub/x/supersedes https://w3id.org/np/RA0cxDyf2c5KPzhRG4FFikLxasJQNI8Ep7R5ulFG6UNRU https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAoTD7udB2KtUuOuAe74tJi1t3VzK0DyWS7rYVAq1GRvw https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/sig http://purl.org/nanopub/x/hasSignature djrdMajQFKQOrBz2OhZOp/cc8h0hGCtSQJFpSQPGvoEkGXaIDpOMA1ndrv8/kAui5qtzuxGuQln/N4/nLGfZWfvGf1xwWXodXJFoTqs/2fnL/OggOxP+kcy2lTBejgLGNlnuZUzEqjkonQBcI4uAyiYBmHaDoY8STmiPxs6grujZ4Z1a1dZYqnnoxls/DmKHBruRPI++f/FLaVaPczWXBYq4s/bjzrr8rSsH4cxZFhIjnKxGV1/ePF0GujvtYPf0OQhGa2Sc4e3F2hO2OWqpjpOtjCAHRuwlwYc3oeQCYVvTE+y6hJzXSVQUYucUZkPavsZChf9MgBRZCI4XZ/92Qg== https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c https://w3id.org/np/RAuMrlgNUAABQztO_9K0EaDDVgGrPRL5h_Tmqxg1dGK8c/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742