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AbstractObjectiveElectronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).MethodWe developed a mapping method for automaticallydoi:10.1101/587436 fatcat:gn773e7dkbdmhm43gseq2ez6v4