Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
Background In clinical research, the primary interest is often the time until occurrence of an
adverse event, ie, survival analysis. Its application to electronic health records is challenging
for two main reasons:(1) patient records are comprised of high-dimensional feature vectors,
and (2) feature vectors are a mix of categorical and real-valued features, which implies
varying statistical properties among features. To learn from high-dimensional data,
researchers can choose from a wide range of methods in the fields of feature selection and …
adverse event, ie, survival analysis. Its application to electronic health records is challenging
for two main reasons:(1) patient records are comprised of high-dimensional feature vectors,
and (2) feature vectors are a mix of categorical and real-valued features, which implies
varying statistical properties among features. To learn from high-dimensional data,
researchers can choose from a wide range of methods in the fields of feature selection and …
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