Predicting all-cause risk of 30-day hospital readmission using artificial neural networks

Mehdi Jamei, Aleksandr Nisnevich, Everett Wetchler, Sylvia Sudat, Eric Liu, Chris T. Bauch
<span title="2017-07-14">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;
Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission.
more &raquo; ... However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions. OPEN ACCESS Citation: Jamei M, Nisnevich A, Wetchler E, Sudat S, Liu E (2017) Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS ONE 12(7): e0181173. https://doi.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0181173">doi:10.1371/journal.pone.0181173</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28708848">pmid:28708848</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5510858/">pmcid:PMC5510858</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h5kiczd5vzbkbplmhkzyf6nezi">fatcat:h5kiczd5vzbkbplmhkzyf6nezi</a> </span>
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