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Analysing repeated hospital readmissions using data mining techniques

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft’s AZURE machine learning software and compare five classification methods: Logistic Regression, Boost...

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書誌詳細
出版年:Health Syst (Basingstoke)
主要な著者: Ben-Assuli, Ofir, Padman, Rema
フォーマット: Artigo
言語:Inglês
出版事項: Taylor & Francis 2017
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC6452839/
https://ncbi.nlm.nih.gov/pubmed/31214343
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/20476965.2017.1390635
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