We are developing methods to use healthcare data to track and monitor emergency care for self-injurious thoughts and behaviors (SITB) among children and teens. Our work was featured at the 2023 American Medical Informatics Association Annual (AMIA) Summit and received the Clinical Research Informatics Distinguished Paper Award.

Our team found that using suicide-related diagnostic codes miss nearly one-third of SITB cases, while suicide-related chief complaints miss more than half. Detection accuracy vary based on the child’s gender and age, with lower sensitivity for male children and preteens compared to female children and adolescents. Certain types of SITB, like preparatory acts and attempts, are also more challenging to detect accurately.

To improve detection, we are developing and validating machine learning models using natural language processing of clinical notes, structured health record data, and gold standard classifications by experts. Early results suggest these models significantly improve sensitivity compared to those relying solely on suicide-related codes and chief complaints. Read our research paper published in JMIR Mental Health. 

Support for this research comes from: