Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 17; p. 5624
• Main Authors: Chu, Jiawei, Kan, Xiu, Che, Yan, Song, Wanqing, Aleksey, Kudreyko, Dong, Zhengyuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.08.2024
• Subjects: Accuracy; Algorithms; China; Data processing; Datasets; Decision making; Deep learning; East Asian People; Electronic Health Records; electronic medical records; Humans; information fusion; Language; Leukocytes; Machine learning; Medical records; Medical research; named entity recognition; Natural language processing; Rehabilitation - methods; rehabilitation robots; Robotics - methods; Robots; Semantics; China; deep learning; named entity recognition; electronic medical records; information fusion; rehabilitation robots
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24175624

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.