Combined Correlational Network for Identifying Behavioral Health Cases from First Responder Report

Behavioral health is a broad term which encompasses our overall mental and physical well-being. Behavioral health issues do not just impact an individual’s life, they also impact wider society. First responders are often the first point of contact for individuals facing emergency. Reports from first responders are a great resource to identify potential behavioral health cases. Manually identifying these cases are time consuming and prone to error. Automation in identifying these reports would be a huge time saver for facilities and allow for them to spend more time on addressing these problems or on more pressing matters. Natural language processing techniques are widely used by researchers for analyzing texts. However, due to the linguistic complexity of the first responder reports it is challenging for traditional natural language processing and deep learning technique to identify these reports. To address this issues in this work we proposed a novel correlational network which utilizes domain experts knowledge along with their correlational relationships to appropriately identify behavioral health cases. Our proposed framework uses the correlation between features and combines them which allows for granular and broad scope insights into a report. Moreover, we performed multi-label classification which reflects the real world scenarios where reports can contain multiple classes. We performed extensive evaluation to determine the efficiency and efficacy of our model. Furthermore, our proposed model outperformed the state-of-the-art model with an accuracy of 72%.