Artificial Intelligence and Robotics Commons^™

A Classifier To Evaluate Language Specificity In Medical Documents, Trudi Miller '08, Gondy A. Leroy, Samir Chatterjee, Jie Fan, Brian Thoms '09

CGU Faculty Publications and Research

Consumer health information written by health care professionals is often inaccessible to the consumers it is written for. Traditional readability formulas examine syntactic features like sentence length and number of syllables, ignoring the target audience's grasp of the words themselves. The use of specialized vocabulary disrupts the understanding of patients with low reading skills, causing a decrease in comprehension. A naive Bayes classifier for three levels of increasing medical terminology specificity (consumer/patient, novice health learner, medical professional) was created with a lexicon generated from a representative medical corpus. Ninety-six percent accuracy in classification was attained. The classifier was then applied …