Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Stephen F. Weng, Jenna Reps, Joe Kai, Jonathan M. Garibaldi, Nadeem Qureshi, Bin Liu
<span title="2017-04-04">2017</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machinelearning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. OPEN ACCESS Citation: Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk
more &raquo; ... using routine clinical data? PLoS ONE 12(4): e0174944. https://doi.org/ 10.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0174944">doi:10.1371/journal.pone.0174944</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28376093">pmid:28376093</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5380334/">pmcid:PMC5380334</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u7tjut5c3zgojdvtl7r5sbadte">fatcat:u7tjut5c3zgojdvtl7r5sbadte</a> </span>
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