Innovations in Time Related Expression Recognition Using LSTM Networks

Abstract

The proposed architecture leverages the strengths of both Convolutional Neural Network (CNN) and Bidirectional Long Short-Term (BLSTM) to create a robust model for temporal expression recognition in clinical texts. The CNN component effectively captures morphological and orthographic features at the character level, which enriches the semantic understanding of complex medical terminologies that are often abbreviated or have unique suffixes and prefixes. The BLSTM component excels in capturing long-range dependencies in text, which is crucial for understanding the context in which temporal expressions occur. By integrating these models with a CRF layer, the system not only predicts discrete labels but also ensures that the sequence of predicted labels is coherent and contextually appropriate, addressing the limitations of models that predict labels independently. The integration of pre-trained biomedical word vectors provides significant contextual grounding tailored to the medical domain, enhancing the model's ability to discern and interpret the nuances of medical language. This is crucial in clinical contexts where accurate interpretation of temporal phrases can be critical for patient management and treatment timelines. Further, experiments conducted on the dataset validate the effectiveness of the proposed model, demonstrating a notable improvement over traditional methods that rely heavily on hand-crafted features and rule-based approaches. Future work could explore the adaptability of this model to other subdomains of the medical field and its efficacy in processing multilingual texts, potentially increasing its applicability in global healthcare settings, with further refinement of the neural architecture and optimization of training strategies potentially yielding even better performance and faster processing times essential for real-time clinical decision support systems.