Multi-mode fusion lung cancer patient curative effect prediction method, system and device and medium

Abstract

The invention discloses a multi-modal fusion lung cancer patient curative effect prediction method, system and device and a medium. The method comprises the following steps: acquiring first patient information of a first lung cancer patient; performing data preprocessing on the first patient information to obtain second patient information; respectively inputting the second patient information into a plurality of preset neural network models for feature extraction to obtain a face feature, a CT image feature, a pathological feature, a pulse feature and a clinical feature of the first lung cancer patient; performing feature fusion on the face feature, the CT image feature, the pathological feature, the pulse feature and the clinical feature according to a preset weight to obtain a multi-modal fusion feature, and inputting the multi-modal fusion feature into a pre-trained lung cancer patient curative effect prediction model to obtain a curative effect prediction result of the first lung cancer patient. The method improves the comprehensiveness of curative effect prediction of the lung cancer patient and the accuracy of the prognosis lifetime of the lung cancer patient, and can be widely applied to the technical field of artificial intelligence.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, system, device and medium for predicting curative effect of lung cancer patients with multimodal fusion. Background technique [0002] One way to realize the modernization of traditional Chinese medicine is to combine CT, pathological images, medical record data, vital sign data, and traditional Chinese medicine "pulse syndrome treatment" data involved in the process of preventing and treating lung cancer in traditional Chinese medicine with the help of new technologies such as artificial intelligence and radiomics technology. The modal clinical data is integrated for feature extraction and fusion, so as to mine the characteristics of the dominant population and build a curative effect prediction model. [0003] In the prior art, most of the modeling predictions are based on a certain data modality of lung cancer patients, and there is a lack of ...

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Problems solved by technology

[0003] In the prior art, most of the modeling predictions are based on a certain data modality of lung cancer patients, and there is a lack of a method that includes as many modality information as possible and can accurately predict the prognosis and survival of lung cancer patients

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