James E. Endicott

Learn More
We are developing algorithms for semi-automated simplification of medical text. Based on lexical and grammatical corpus analysis, we identified a new metric, term familiarity, to help estimate text difficulty. We developed an algorithm that uses term familiarity to identify difficult text and select easier alternatives from lexical resources such as(More)
Measuring text difficulty is prevalent in health informatics since it is useful for information personalization and optimization. Unfortunately, it is uncertain how best to compute difficulty so that it relates to reader understanding. We aim to create computational, evidence-based metrics of perceived and actual text difficulty. We start with a corpus(More)
With increasing text digitization, digital libraries can personalize materials for individuals with different education levels and language skills. To this end, documents need meta-information describing their difficulty level. Previous attempts at such labeling used readability formulas but the formulas have not been validated with modern texts and their(More)
BACKGROUND Adequate health literacy is important for people to maintain good health and manage diseases and injuries. Educational text, either retrieved from the Internet or provided by a doctor's office, is a popular method to communicate health-related information. Unfortunately, it is difficult to write text that is easy to understand, and existing(More)
  • 1