Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.

Introduction: In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain. Methods: To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest. Results and discussion: Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

Development of a functional and emotional measure of dysmenorrhea (FEMD) in Chinese university women.

Primary dysmenorrhea, or cramps, causes pain, limits activity, and increases emotional tension in young women, but its measurement has not received enough research attention. We have developed a functional and emotional measure of dysmenorrhea (FEMD, 14 items), and trialed it with a sample of 833 Chinese university women. Two factors (scales) were extracted by principal component analysis (PCA) and subsequently approved by a confirmatory factor analysis (CFA). The two scales were intercorrelated and were correlated with experienced pain severity and, to a lesser degree, with the depressive tendency. We have demonstrated that FEMD has stable components that might help measure dysmenorrhea-related dysfunctions.