Feb. 12, 2024, 5:42 a.m. | Neslihan Suzen Evgeny M. Mirkes Damian Roland Jeremy Levesley Alexander N. Gorban Tim J. Coats

cs.LG updates on arXiv.org arxiv.org

Electronic patient records (EPRs) produce a wealth of data but contain significant missing information. Understanding and handling this missing data is an important part of clinical data analysis and if left unaddressed could result in bias in analysis and distortion in critical conclusions. Missing data may be linked to health care professional practice patterns and imputation of missing data can increase the validity of clinical decisions. This study focuses on statistical approaches for understanding and interpreting the missing data and …

analysis bias clinical cs.ai cs.cl cs.hc cs.it cs.lg dark matter data data analysis electronic information math.it matter medical medicine part patient practices records understanding wealth

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