Extracting Phenotypes from Patient Claim Records Using Nonnegative Tensor Factorization
نویسندگان
چکیده
منابع مشابه
Limestone: High-throughput candidate phenotype generation via tensor factorization
The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to medical concepts that clinical researchers need or use. Some recent studies have fo...
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Unsupervised phenotyping is about clustering patients into homogeneous groups (“phenotypes”) based on observed clinical features. An open challenge is to provide a principled way to define those patient phenotypes in a simple and clinically meaningful manner. In this work, we tackle this challenge by proposing a nonnegative matrix factorization approach extracting phenotypes as a set of integer...
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OBJECTIVE Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling...
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This paper presents a modified digital image watermarking method based on nonnegative matrix factorization. Firstly, host image is factorized to the product of three nonnegative matrices. Then, the centric matrix is transferred to discrete cosine transform domain. Watermark is embedded in low frequency band of this matrix and next, the reverse of the transform is computed. Finally, watermarked ...
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تاریخ انتشار 2014