A natural language data enhancement model and method for postoperative risk prediction

Abstract

The invention discloses a natural language data enhancement model and method for postoperative risk prediction. The natural language data is converted into a vector through the pre-training model MedBERT obtained under the training of the data set in the medical field. The discrete variables in the tabular data are also converted into vectors by means of entity embedding, and for the two different types of data, the multi-head self-attention method is selected to fuse them. The attention mechanism algorithm extracts the correlation between features, and screens out important features for prediction, so that the key information in the natural language data can be associated with the key information in the tabular data, and the purpose of multi-type information fusion is achieved. For the first time, the invention incorporates natural language data into the task of postoperative risk prediction.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a natural language data enhancement model and method for postoperative risk prediction. Background technique [0002] Postoperative risk estimation is often viewed as a dichotomous task. Statistical machine learning models are widely used to solve this problem, such as Logistic Regression (ession, LR) and Extreme Gradient Boosting (eXtreme Gradient Boosting, XGBoost). The vector-based LR method normalizes both discrete and continuous variables and inputs them into the model, while the tree-based XGBoost model directly uses structured data for training. [0003] In recent research work, many researchers have begun to use deep learning to solve the problem of predicting postoperative risk of patients because of its own complex feature representation ability and predictive performance. In these studies, perioperative tabular data was the main source of data, which c...

AI Technical Summary

Problems solved by technology

The directly spliced ​​vectors will have the problem of information redundancy. When the vectors containing irrelevant information have high latitude, and the vectors containing important information have low latitude, splicing them will make the redundant information occupy the majority, resulting in the truly critical important information is ignored

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