Patient screening and marking method based on partial multi-mark learning

Abstract

The invention belongs to the field of partial multi-mark learning and data mining, and particularly relates to a patient screening and marking method based on partial multi-mark learning. The method comprises the following steps: acquiring pathological sample data of a patient, inputting the pathological sample data into a trained medical text semantic information big data prediction model based on partial multi-mark learning, predicting a disease type and a disease probability of the patient, and marking the patient according to the disease type and the disease probability of the patient. According to the method, the problem of classification class imbalance is further processed, a more accurate marking result can be predicted, a patient can perform health management according to the marking result, a doctor can perform next diagnosis on the patient according to the result, and the method has good social benefits and economic benefits.

Description

technical field [0001] The invention belongs to the fields of multi-label learning and data mining, and in particular relates to a patient screening and marking method based on multi-label learning. Background technique [0002] It is difficult to directly obtain a large amount of labeled data in the real world, and the huge scale of the labeled data and the lack of professional knowledge often make manual labeling very expensive. For example, medical image annotation requires rich domain knowledge; in fact, in the real world, weakly supervised information is easier to obtain than strongly supervised information, and at the same time, weakly supervised information is more directional than unsupervised information in the learning stage. Multi-label learning has gradually become an important weakly supervised machine learning framework. In multi-label learning, each example corresponds to a set of candidate labels, and multiple real labels are hidden in the set of candidate la...

AI Technical Summary

Problems solved by technology

Category imbalance is also called data skew, which refers to the situation that the number of training samples of different categories in the classification task is very different. In the existing technology, samples with category imbalance will cause the training model to focus on the category with a large number of samples, while ignoring the number of samples Fewer categories, so the generalization ability of the model will be affected, thereby affecting the accuracy of the diagnosis result

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