spm_to_df

Provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Thomas Charlon

nlpembeds

Natural Language Processing Embeddings

Doudou Zhou

CELEHS 

spm_to_df function

<dl><dt>spm</dt>
<dd>Sparse matrix</dd></dl>

Arguments

Write sparse matrix to dataframe — spm_to_df

<dl>

<dt>spm</dt>
<dd>Sparse matrix</dd>

</dl>

spm_to_df: Write sparse matrix to dataframe

Description

Usage

Value

Arguments