## 4 Empirical Studies
### Keyword Feature Expansion  
1. Data pre-processing  
The cut algorithm of posseg in the Jieba library is used in this experiment, which is equivalent to using the exact pattern to split words, and it also annotates the lexical properties of each word, such as noun \"n\", verb \"v\", adjective \"a\", etc. This method defaults to using the Hidden Markov Model to split words that are not logged in the lexicon, which is a bit better compared to the others. The deactivation word list used in the experiment is a combination of four deactivation word lists: the Baidu deactivation word list, the HIT deactivation word list, the Sichuan University Machine Intelligence Laboratory deactivation thesaurus, and the Chinese deactivation word list. For example, the word \"easy\" in the final result of the word division in the table is only a word describing the degree of the patient's condition. For example, \"easy\" in the final result of the table is only  
Figure 8: ROC curve. a word describing the degree, which is not a useful word for the patient's description of the condition, so it is eliminated after the deactivation process. 2. Keyword extraction  
Text pre-processing is the primary step when processing medical texts. This step aims to transform each medical text into independent units of analysis and integrate them into a unified text collection. Subsequently, the text collection is processed by the TF-IWF algorithm to identify and extract keywords. It is worth noting that the TF-IWF algorithm is more advantageous than the traditional TF-IDF algorithm in professional text category differentiation due to the fact that it pays more attention to the rarity of words in specific categories of text. Taking nephrology text analysis as an example, TF-IWF can accurately identify professional terms with high relevance and diagnostic value for specific categories of text, such as \"urinary protein\", \"renal failure\", etc. In comparison with TF-IDF, TF-IDF is more effective in distinguishing professional text categories. In contrast, the TF-IDF algorithm is more inclined to focus on generalized terms that appear frequently in all documents, such as \"patient\" and \"symptoms\". Based on the excellent performance of the TF-IWF algorithm in keyword extraction, this paper adopts this algorithm to extract keywords and ranks the keywords in descending order based on the TF-IWF values. Finally, this paper selects the words with higher TF-IWF values from the candidate keywords and constructs a reserved dictionary containing 800 entries. 3. Keyword expansion results  
After completing the construction of the keyword dictionary, the Word2 Vec model was chosen in this study to match and analyze the lexical dictionary as well as the segmentation results of the medical short text. The model training parameters are as follows: sg = 0, vector_Size = 100, epochs = 5, window = 5, min_count = 1, and workers = 4. By filtering the words with the highest similarity to the keywords of the text, the keywords of the medical text were successfully expanded, and the depth and breadth of the semantic level were significantly improved. In order to ensure the simplicity and efficiency of the expanded content and avoid redundancy of information, the number of expanded keywords was strictly limited in this study. Taking \"hypertension and dizziness\" as an example, the Word2 Vec model only extracts [hypertension, dizziness] as an input feature, which fails to fully consider the sequential relationship between the words, thus restricting the model's ability to accurately grasp the deeper meaning of the text to a certain extent. However, through in-depth analysis of other sentences in the dataset, e.g., \"high blood pressure, chest tightness, cause\" and \"shortness of breath because of high blood pressure,\" the model successfully identifies \"chest tightness\" and \"shortness of breath\" as two words that are associated with \"high blood pressure\".