Word2Vec inversion and traditional text classifiers for phenotyping lupus.BACKGROUND
 These matrices were subjected to several different NLP classifiers: neural networks, random forests, naïve Bayes, support vector machines, and Word2Vec inversion, a Bayesian inversion method.  We calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00% with an AUC of 0.900, the shallow neural network with CUIs to be 92.10% with an AUC of 0.970, the random forest with BOWs to be 95.25% with an AUC of 0.994, the random forest with CUIs to be 95.00% with an AUC of 0.979, and the Word2Vec inversion to be 90.03% with an AUC of 0.905.


 The Word2Vec inversion method failed to significantly beat the ICD-9 code classification, but yielded promising results.  The Word2Vec inversion is hypothesized to become more powerful with access to more data. 
Word2Vec