Text Mining Strategies: RoBERTa Optimization for Efficient Pain Assessment in Hospice Care

Abstract

The hospice unit in medical care offers comprehensive, personalized care to patients, yet the recent epidemic and associated illnesses have strained medical resources, leading to a shortage in capacity. The necessity for frequent physiological documentation and patient assessments places a considerable burden on the nursing staff, particularly in the context of limited personnel. This study addresses this challenge by leveraging natural language processing to aid in the evaluation of pain indices, aiming to enhance implementation quality and reduce associated costs. Three BERT models— BERT, MacBERT, and RoBERTa were employed for training purposes. Among these models, RoBERTa demonstrated exceptional performance, achieving an impressive accuracy rate of 99%. This research highlights the potential of natural language processing tools, specifically the RoBERTa model, in alleviating the workload of nursing staff and improving the efficiency of pain assessment in hospice care during times of heightened demand and limited resources.