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Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications
OBJECTIVE: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN: Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. PATIENTS: 50,318 adult patients undergoing major surgery. MEASUREMENTS...
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| Pubblicato in: | PLoS One |
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| Autori principali: | , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Public Library of Science
2016
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4883761/ https://ncbi.nlm.nih.gov/pubmed/27232332 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0155705 |
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