Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in u nderstanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language p rocessing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health profess ionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.
There is concordance among clerkship directors regarding the importance of identifying unprofessional behaviors among medical students although there exists a range of modalities for monitoring, remediating, and disciplining such behaviors.
The implementation of the PCX during the psychiatry clerkship significantly improved student performance on several important components of the CCX, which is used to prepare students for the Step 2 CSA. This suggests that the use of standardized patients during the clerkship may help students improve their interviewing and interpersonal skills.
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