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Evaluating Patient Readmission Risk: A Predictive Analytics Approach
[article]
<span title="2018-12-11">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2019-08-21">2019</span>
<span class="release-stage">accepted</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.11028v2">arXiv:1812.11028v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iy5z74ohozc2rkdwqlthdxvchi">fatcat:iy5z74ohozc2rkdwqlthdxvchi</a> </span>
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With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.11028v1">arXiv:1812.11028v1</a>
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... develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.
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