# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 1 Introduction
[PERSON] and [PERSON] [13] suggested an algorithm that combines texts and graphs to repurpose drug usage. [PERSON] et al. [14] proposed a machine knowledge graph based on an enhanced transformer. [PERSON] et al. [15] introduced a multi-model approach to detect disease-disease relations. However, text-based approaches are used broadly to determine associations between several aspects of medical texts. For example, even though some technical issues need resolving, BERT is widely used in medical texts to derive insights and patterns. Some up-to-date references can be found in [16; 17; 18]. In this paper, an algorithm is proposed to construct a graph database of medical texts, by employing ALBERT+Bi-LSTM+CRF to resolve the issues outlined. An in-depth analysis and training on medical texts were conducted to gain key insights and extract patterns, using advanced statistical learning methods [19]. The proposed algorithm improves the efficiency of medical text processing and provides medical doctors with more accurate auxiliary diagnostic and treatment suggestions, thus contributing to advancing the overall quality of medical services. Figure 1 depicts the steps taken. Firstly, medical texts with annotations are obtained from public datasets and manually labeled. Then, those are preprocessed to remove some special symbols and check the accuracy of the annotation format. Next, the whole dataset is split into three, for training, validation, and testing, respectively. The ALBERT+Bi-LSTM+CRF algorithm is used for training. A large number of medical texts or medical guidelines can be embedded into the proposed algorithm as inputs to generate predictions after the algorithm is evaluated and adjusted. Afterward, a structured entity set and relationship set are obtained. These data are organized and fused to form a ternary group after duplicates are removed. Finally, the database of a knowledge graph is constructed. This study applied the knowledge graph concept in the medical field by employing the formal expression of the Semantic Web. In this way, the implicit semantics in the data are more effectively captured and understood, which provides strong support for information retrieval and personalized medical services. This study also employed statistical methods to conduct comprehensive training on various medical texts, aiming to derive effective insights and patterns from the texts and construct a structured and logically rigorous database for knowledge graphs. According to the findings, significant academic value and application prospects are demonstrated. The structure of this article is as follows: Section 2 presents the core concepts, construction stage, and architecture of the knowledge graph. Section 3 addresses model training and presents the strengths and limitations of the ALBERT algorithm, while the Bi-LSTM+CRF algorithm is proposed to enhance the precision and help extract key texts effectively. Section 4 presents the experimentation and optimization phases. Critical steps such as real medical text annotation, data preprocessing, label definition, and extraction of the entity database are outlined, and the results and analyses are presented. Section 5 summarizes the work conducted. The proposed algorithm, ALBERT+Bi-LSTM+CRF, and its further potential advancement opportunities are discussed regarding the scale of the dataset, the introduction of medical image data, and the label structure of the knowledge graph.