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Title: Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes
Authors: Mirkes, E. M.
Coats, Timothy J.
Levesley, J.
Gorban, A. N.
First Published: 8-Jun-2016
Publisher: Elsevier for Pergamon
Citation: Computers in Biology and Medicine, 2016, 75, pp. 203-216
Abstract: Handling of missed data is one of the main tasks in data preprocessing especially in large public service datasets. We have analysed data from the Trauma Audit and Research Network (TARN) database, the largest trauma database in Europe. For the analysis we used 165,559 trauma cases. Among them, there are 19,289 cases (11.35%) with unknown outcome. We have demonstrated that these outcomes are not missed 'completely at random' and, hence, it is impossible just to exclude these cases from analysis despite the large amount of available data. We have developed a system of non-stationary Markov models for the handling of missed outcomes and validated these models on the data of 15,437 patients which arrived into TARN hospitals later than 24h but within 30days from injury. We used these Markov models for the analysis of mortality. In particular, we corrected the observed fraction of death. Two naïve approaches give 7.20% (available case study) or 6.36% (if we assume that all unknown outcomes are 'alive'). The corrected value is 6.78%. Following the seminal paper of Trunkey (1983 [15]) the multimodality of mortality curves has become a much discussed idea. For the whole analysed TARN dataset the coefficient of mortality monotonically decreases in time but the stratified analysis of the mortality gives a different result: for lower severities the coefficient of mortality is a non-monotonic function of the time after injury and may have maxima at the second and third weeks. The approach developed here can be applied to various healthcare datasets which experience the problem of lost patients and missed outcomes.
DOI Link: 10.1016/j.compbiomed.2016.06.004
ISSN: 0010-4825
eISSN: 1879-0534
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier, 2016. This version of the article is distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License ( ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Published Articles, Dept. of Cardiovascular Sciences

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