Domain Knowledge-Driven Multi-Label Behavioral Health Identification from Police Report

• Published in: IEEE International Conference on Big Data pp. 6904 - 6913
• Main Authors: Azmee, Abm Adnan, Nweke, Francis, Khan, Md Abdullah Al Hafiz, Pei, Yong, Thomas, Dominic, Nandan, Monica
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.12.2024
• Subjects: 911 call; behavioral health; Collaboration; Data models; Deep learning; first responder; Knowledge based systems; Law enforcement; Manuals; Multi label classification; Natural language processing; public narrative; Subject matter experts; Transformers
• ISSN: 2573-2978
• DOI: 10.1109/BigData62323.2024.10825617

Behavioral health plays a pivotal role in individuals' overall quality of life. Timely identification and intervention of behavioral health concerns are essential for building a supportive community. In case of emergencies, first responders (police, fire, EMT) provide support to the community. The report of the first responders contains valuable cues about individuals struggling with behavioral health concerns. Currently, first responders manually screen these reports to determine individuals with behavioral health concerns. Manual screening of these reports is time-consuming and prone to error. Automated analysis of these reports is essential for providing timely intervention. Natural language processing techniques demonstrated potential for automated analysis of textual data. However, due to the unstructured and complex nature of the data, it's challenging for traditional deep learning and natural language processing techniques to identify these cases. Moreover, support from subject matter experts is essential for the effective identification of these cases. In this research, we have developed a multi-label behavioral health detection framework that utilizes domain knowledge from subject matter experts for the effective identification of behavioral health cases. In addition, we have expanded the domain knowledge base by recognizing new behavioral health-related terms. We validated the efficacy and effectiveness of our proposed model through a comprehensive evaluation of real-world data. Furthermore, our proposed model demonstrated 6-24% performance improvement over the state-of-the-art models.