Clinical Concept Embeddings Trained on Health Insurance Claims, Clinical Narratives from Stanford and PubMed Journal Articles

Represent a clinical concept as a vector

Released in 2018 by researchers at Harvard Medical School and the University of North Carolina, this model provides clinical concept embeddings for roughly 110,000 medical concepts, and equals or exceeds the state of the art on all benchmarks. Concepts were mapped into a common co-occurrence space so as to produce a single embedding.

Number of layers: 1 | Parameter count: 54,527,000 | Trained size: 218 MB |

Training Set Information

Clinical Concepts from Massive Sources of Medical Data, consisting of data from three independent sources: insurance claims for 60 million Americans from 2008-2015, clinical notes from 20 million patients at Stanford hospitals and clinics and 1.7 million full-text PubMed articles.

Training Set Data

Clinical Concepts from Massive Sources of Medical Data

Examples

Download Example Notebook

Open in Wolfram Cloud

Resource retrieval

Get the pre-trained net:

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$NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"]$

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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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$vector = NetModel[ "Clinical Concept Embeddings Trained on Health Insurance Claims, \ Clinical Narratives from Stanford and PubMed Journal Articles"][apnea]$

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Use the embedding layer inside a NetChain:

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$chain = NetChain[{NetModel[ "Clinical Concept Embeddings Trained on Health Insurance Claims, \ Clinical Narratives from Stanford and PubMed Journal Articles"], LongShortTermMemoryLayer[10]}]$

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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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$FeatureSpacePlot[Join[bacteria, viruses], FeatureExtractor -> NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"]]$

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Visualize the associated embeddings in three dimensions:

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$FeatureSpacePlot3D[Join[bacteria, viruses], FeatureExtractor -> NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"]]$

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Word analogies

Get the pre-trained net:

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$net = NetModel[ "Clinical Concept Embeddings Trained on Health Insurance Claims, \ Clinical Narratives from Stanford and PubMed Journal Articles"]$

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Get a list of concept unique identifiers:

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Obtain the embeddings:

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Create an association whose keys are concept unique identifiers and whose values are vectors:

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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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$NetInformation[ NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"], "ArraysElementCounts"]$

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Obtain the total number of parameters:

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$NetInformation[ NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"], "ArraysTotalElementCount"]$

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Obtain the layer type counts:

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$NetInformation[ NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"], "LayerTypeCounts"]$

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Export to MXNet

Export the net into a format that can be opened in MXNet:

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$jsonPath = Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], NetModel["Clinical Concept Embeddings Trained on Health Insurance \ Claims, Clinical Narratives from Stanford and PubMed Journal \ Articles"], "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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$ResourceObject[ "Clinical Concept Embeddings Trained on Health Insurance Claims, \ Clinical Narratives from Stanford and PubMed Journal \ Articles"]["ByteCount"]$

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Represent the MXNet net as a graph:

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Construction Notebook

Download Construction Notebook

Open in Wolfram Cloud

Requirements

Wolfram Language 11.3 (March 2018) or above

Resource History

Date Created: 20 June 2018
Latest Update: 19 July 2018

Reference

A.L. Beam et al.,
"Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data," arXiv:1804.01486 (2018)
(available from https://figshare.com/s/00d69861786cd0156d81)
Rights: Unrestricted use