Parameter Efficient Transfer Learning for Suicide Attempt and Ideation Detection

Abstract

Pre-trained language models (LMs) have been deployed as the state-of-the-art natural language processing (NLP) approaches for multiple clinical applications. Model generalisability is important in clinical domain due to the low available resources. In this study, we evaluated transfer learning techniques for an important clinical application: detecting suicide attempt (SA) and suicide ideation (SI) in electronic health records (EHRs). Using the annotation guideline provided by the authors of ScAN, we annotated two EHR datasets from different hospitals. We then fine-tuned ScANER, a publicly available SA and SI detection model, to evaluate five different parameter efficient transfer learning techniques, such as adapter-based learning and soft-prompt tuning, on the two datasets. Without any fine-tuning, ScANER achieve macro F1-scores of 0.85 and 0.87 for SA and SI evidence detection across the two datasets. We observed that by fine-tuning less than ~2% of ScANER’s parameters, we were able to further improve the macro F1-score for SA-SI evidence detection by 3% and 5% for the two EHR datasets. Our results show that parameter-efficient transfer learning methods can help improve the performance of publicly available clinical models on new hospital datasets with few annotations.