Medical aid decision model interpretation method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention relates to a medical aid decision model interpretation method and device, a storage medium and electronic equipment, and relates to the technical field of big data processing, the method comprises the following steps: obtaining input data of a medical aid decision model and a data prediction result corresponding to the input data, generating a feature subset according to the sub-features included in the input data; combining the target features included in the feature subset to obtain a feature pair, and mining the value domain of the feature value of each target feature included in the feature pair to obtain a candidate value domain of each target feature; fusing the to-be-fused value domain into a current value domain corresponding to the target feature ina preset initial rule base to obtain a target rule base; and matching the decision path from the input data to the prediction result in a target rule base, and explaining the medical aid decision model according to a target rule included in the decision path. According to the embodiment of the invention, the interpretability of the medical aid decision-making model is enhanced.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of big data processing, and in particular, relate to a method for interpreting an auxiliary medical decision-making model, an interpretation device for an auxiliary medical decision-making model, a computer-readable storage medium, and electronic equipment. Background technique [0002] With the development of artificial intelligence technology, machine learning has a wide range of applications in retail, medical care, finance, autonomous driving and other fields. However, many machine learning models are similar to "black boxes". After inputting data, they can output results, but they are not explanatory. Users cannot understand their internal decision-making mechanisms and cannot meet the needs of business scenarios. [0003] In order to solve the above problems, the prior art adopts the following method to explain the model: through a visualized SHAP scheme, the contribution degr...

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Problems solved by technology

However, as a model interpretation development package, SHAP only provides general basic model interpretation functions, and does not specifically optimize the model in the field of medical assistance decision-making, so the interpretability of the medical assistance decision-making model will be weak; at the same time, the interpretability of SHAP Still limited to the feature level, the contribution of mining features still cannot explain the decision-making process of deep learning

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