Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes

For many clinical conditions, only a small number of patients experience adverse outcomes. Developing risk stratification algorithms for these conditions typically requires collecting large volumes of data to capture enough positive and negative for training. This process is slow, expensive, and may...

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Bibliografiske detaljer
Main Authors:	Syed, Zeeshan, Saeed, Mohammed, Rubinfeld, Ilan
Format:	Artigo
Sprog:	Inglês
Udgivet:	American Medical Informatics Association 2010
Fag:	Articles
Online adgang:	https://ncbi.nlm.nih.gov/pmc/articles/PMC3041411/ https://ncbi.nlm.nih.gov/pubmed/21347083
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Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes

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