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![]() ![]() ![]() The two annotators reviewed the ratings to assemble a single adjudicated set of ratings, from which a support vector machine (SVM) based document classifier was trained. For natural language processing (NLP) analysis, a set of retrieval criteria was developed for documents expected to have a high correlation to SSc. Methods The electronic medical data for this study came from Veterans Informatics and Computing Infrastructure (VINCI). The objective of this project was to use informatics to identify potential SSc patients in the VHA that were on prednisone, in order to inform an outreach project to prevent scleroderma renal crisis (SRC). Rhiannon, Julia Zeng, Qing T.īackground Electronic medical records (EMR) provide an ideal opportunity for the detection, diagnosis, and management of systemic sclerosis ( SSc) patients within the Veterans Health Administration (VHA). ![]() Informatics can identify systemic sclerosis ( SSc) patients at risk for scleroderma renal crisis ![]()
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