Resource retrieval
Get the pre-trained net:
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Basic usage
This net represents clinical concepts as 500-dimensional vectors. Concepts are identified by their concept unique identifier (CUI). The mapping between identifiers and human-readable concepts can be obtained from the clinical concepts ResourceData:
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Find the concept unique identifier for the clinical concept "apnea":
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Use the net to obtain the associated embedded vector:
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Use the embedding layer inside a NetChain:
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Feature visualization
Create two lists of related concepts:
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Visualize the associated embeddings in two dimensions:
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Visualize the associated embeddings in three dimensions:
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Word analogies
Get the pre-trained net:
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Get a list of concept unique identifiers:
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Obtain the embeddings:
Create an association whose keys are concept unique identifiers and whose values are vectors:
Find the concept unique identifier for the clinical concept "DNA virus":
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Find the five nearest concept unique identifiers to "DNA virus":
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Obtain the human-readable concept labels for these concept unique identifiers:
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Explore similar drugs to a given one. Find the concept unique identifier for "metronidazole":
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Find the five nearest concept unique identifiers to "metronidazole":
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Obtain the human-readable concept labels for these concept unique identifiers:
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Identify comorbidity relationships: a comorbidity is a disease or condition that frequently accompanies the primary diagnosis. A comorbidity for the condition "premature infant" is "bronchopulmonary dysplasia." Comorbidities of another condition--for example, obesity--can be investigated using word analogies. First obtain the relevant CUIs:
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"Premature infant" is to "bronchopulmonary dysplasia" as "obesity" is to:
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Obtain the human-readable concept labels for these concept unique identifiers:
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Net information
Inspect the sizes of all arrays in the net:
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Obtain the total number of parameters:
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Obtain the layer type counts:
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Export to MXNet
Export the net into a format that can be opened in MXNet:
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Export also creates a net.params file containing parameters:
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Get the size of the parameter file:
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The size is similar to the byte count of the resource object:
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Represent the MXNet net as a graph:
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