# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 5 Conclusions and Future Prospects
### Conclusions  
Despite the outstanding achievements, there is still room for further progress. First, public datasets are generally relied on when training. However, the proposed algorithm needs to include medical clinical texts derived from real cases, so the proposed algorithm can be closely aligned with clinical practices and so the hospital's diagnosis and treatment processes are more accurately reflected. The algorithm has been verified with real, manually annotated, small-scale medical texts [32]. The scale of the implemented data needs to be further improved to produce a more feasible algorithm that can be expanded to achieve practicality and accuracy. Therefore, future development work will be focused in this direction. Second, current knowledge graphs primarily focus on text data, but the medical field also possesses abundant medical image data [33]. Thus, a multi-modal knowledge graph should be constructed. The image data often reflect a patient's physical condition more intuitively and in more detail than text reports. Thus, incorporating medical image data into the construction process of knowledge graphs and deeply mining the effective information within them will greatly enrich the content of the knowledge graphs and progress their reasoning capabilities. Finally, the labeling structure of the knowledge graph needs to be further refined [34]. This research merely focuses on the extraction of the categories of basic entities, for instance, diseases. However, a more comprehensive form of extraction is needed in medical applications such as the extraction of symptoms and medications for patient conditions, which is a far more complex setting. For example, to describe a patient's disease course more comprehensively, the development of symptoms from a temporal dimension needs to be tracked, and multi-dimensional datasets should be implemented, which correspond to the patient's demographic and medical attributes. Thus, a more complete and graphical knowledge database with three dimensions should be constructed. The developed algorithm will be applied to real-world scenarios. However, the efficiency of the text annotation process in natural languages needs to be enhanced, and model training with a large number of real medical texts needs to be conducted. Hence, we have attempted to incorporate the methods of large language models (LLMs) into the construction process of the knowledge graph. By leveraging the powerful semantic analysis and generation capabilities of large language models (LLMs), we can efficiently extract entity and relationship triplets from texts generated by natural languages and supplemented by manual reviews to ensure data accuracy. Also, we are actively advancing collaborations with multiple hospitals to find suitable directions for the practical application of knowledge graphs. In addition, one ongoing research study aims to utilize the knowledge graph to predict the likelihood of complications in patients when undergoing percutaneous lung ablation. By collecting patients' basic information, laboratory and examination reports, and ablation surgery information, a knowledge graph is being constructed for lung ablation and its structural features are being analyzed to predict patients' probabilities of developing surgical complications. Future research will focus on the following directions:  
(1) Expand the data scale to generate a more feasible algorithm that has practicality and accuracy in real cases. (2) Construct a multi-modal knowledge graph by integrating medical image data to enrich the content of the knowledge graph and improve its reasoning capabilities. (3) Improve the markup structure of knowledge graphs to achieve more comprehensive extracted forms, such as symptoms and medications related to patients' conditions. (4) Investigate the integration of NLP with medical image recognition by constructing a cross-modal medical knowledge graph to provide more comprehensive and accurate support for medical decision-making.