# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 1 Introduction  
Both internet technology and digitization have altered multiple areas of human life, and the medical field and health sector are no exceptions. Smart healthcare is no longer just a term but aims to optimize the management and operational processes of hospitals and health institutions. Hence, the quality level of medical services for the public is being substantially enhanced [1]. The medical field deals with vast amounts of text data generated as texts written in natural languages, such as medical records, test reports, textbooks, encyclopedias, academic journals, and clinical guidelines. These contain rich, comprehensive, and interrelated professional knowledge and medical practices [2]. Correspondingly, these texts' more effective utilization provides invaluable auxiliary guidance for medical doctors when diagnoses and treatments are administered [3]. Hence, developing methods to efficiently process medical texts and derive key insights and patterns has become the primary research focus [4]. For example, Named Entity Recognition (NER), one of the key approaches for processing medical texts, aims to accurately derive medical entities, such as disease names, symptom descriptions, and the administration of treatments. Generally, a more effective process results in extracting crucial subsequent relationships and analyzing information in a better way. The NER framework mainly includes three types of methods, namely, rule-based and dictionary-based, statistical, and the combined form of these two [5]. Although the rule-based and dictionary-based approaches have been employed for a long time, their application scope is relatively constrained due to their dependence on knowledge bases. Statistical methods are employed to standardize medical text databases by analyzing and learning manually annotated corpora that have become the mainstream direction of current research. The concept of the Semantic Web and the use of ontology models was advocated to formally express the implicit semantics of knowledge graphs of data that prove themselves in several applications [6]. Subsequently, formal models of the Semantic Web emerged, such as the Resource Description Framework (RDF) and Web Ontology Language (WOL), laying a solid foundation for the development of a knowledge graph [7]. As the research has deepened, the knowledge graph method has been progressed. However, it not only depends on the inheritance and development of Semantic Web technology and benchmarks but also involves a substantial breakthrough in information representation and comprehension. Google Inc. (2012) officially launched the Knowledge Graph [8], which enables computers to comprehend human communication patterns. Hence, more intelligent and personalized services are provided [9]. The wave of digitalization adopted in the medical field is transforming medical care unprecedentedly. This new paradigm also brings a wide range of optimized procedures pertinent to hospital management and operations provided by technological means. Thus, the quality of medical services is significantly enhanced [10]. Even though a vast number of textual data records are rich in medical knowledge and practice, they cannot be used directly to their full potential; in order to achieve that, their contents need to be submitted to further crunching processes, to extract useful information. Specifically, one of the reasons why they cannot be used directly is their lack of a unified standard structure. Instead, they have diversified and complex structures [11]. Another requirement is to optimize model sizes while maintaining the precision of the BART algorithm since it is a well-designed algorithm that provides better outcomes. However, some technical issues accompany these efforts. Beyond these, the literature provides alternatives when the research direction turns to deep learning methods. For example, [PERSON] et al. [12] proposed an effective fusion method based on graph neural networks (GNNs) in Chinese medical text classification.