[2S6-OS-7a] OS-7

[2S6-OS-7a-04] Construction of a Multilabel Classifier for Extracting Incident Multiple Factors from Incident Reports Related to Medication Assistance in Residential Care Facilities

〇Hayato Kizaki¹, Sayaka Ebara¹, Hiroki Satoh², Satoko Hori¹, Yasufumi Sawada² (1. Keio University, 2. Univ. of Tokyo)

Keywords:residential care facilities, Incident reports, natural language processing

Medication management in residential care facilities is fraught with challenges, particularly concerning the occurrence of incidents when non-medical staff assist residents with their medications. To mitigate incidents, understanding the root causes is essential, typically through incident report analysis. Our study developed a multi-label classifier to identify factors contributing to medication-related incidents in residential care facilities from 7,121 incident report descriptions. Nine factors were identified: procedure adherence, medication, resident, resident family, non-medical staff, medical staff, team, environment, and organizational management. Multiple labels were assigned to each description. Due to the scarce labels for resident family and non-medical staff, these were excluded for model development. We fine-tuned three pre-trained models (two BERT and one ELECTRA), all achieving promising results. The F1 scores exceeded 0.6 across most categories and exact match accuracy also exceeded 0.6, demonstrating the effectiveness of our model in identifying factors from medication-related incident reports in residential care facilities.