Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records

摘要

BackgroundDistinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-consuming and cumbersome. We compared different approaches to develop an automatic case identification system with high sensitivity to assist manual annotators.