A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naive Bayes algorithms

摘要

Public health surveillance programs in the U.S. are undergoing landmark changes with the availability of electronic health records and advancements in information technology. Injury narratives gathered from hospital records, workers compensation claims or national surveys can be very useful for identifying antecedents to injury or emerging risks. However, classifying narratives manually can become prohibitive for large datasets.