Disease data analysis method based on medical knowledge base and lung cancer risk prediction system

Abstract

The invention discloses a disease data analysis method based on a medical knowledge base and a lung cancer risk prediction system. The method comprises the step of obtaining a disease big data queue from a distributed medical information database according to a medical dictionary; according to a disease classification standard, performing data standardization on the disease big data queue; establishing a lung cancer disease queue based on the disease big data queue; and according to the lung cancer disease queue, screening risk indexes related to lung cancer based on correlation analysis. A risk prediction model can be constructed based on the risk indexes. Based on the medical knowledge base, data extraction and structuralization of disease data and lung cancer related data are realized,and data guarantee is provided for subsequent lung cancer risk prediction.

Description

technical field [0001] The invention belongs to the technical field of medical big data processing, and in particular relates to a disease data analysis method based on a medical knowledge base and a lung cancer risk prediction system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Lung cancer occurs in the epithelium of the bronchial mucosa, also known as bronchial cancer. In the past 50 years, many reports have reported that the incidence of lung cancer has increased significantly. Among male cancer patients, lung cancer ranks first, and the incidence rate of women has increased rapidly, accounting for the second most common malignant tumors in women. digit or 3rd digit. [0004] At present, the auxiliary diagnosis methods for lung cancer mainly focus on image analysis based on deep learning and impact index analysis based on traditio...

AI Technical Summary

Problems solved by technology

Among them, the application of deep learning technology to early lung cancer detection mainly includes data preprocessing and detection of pulmonary nodules using convolutional networks, but these methods mainly focus on identifying lung nodule areas and non-lung nodule areas. On the one hand, The types of pulmonary nodules are not carefully distinguished. On the other hand, the type and probability of pulmonary nodule canceration are closely related to the characteristics of pulmonary nodules and other related disease variables. The traditional detection convolutional network can only get the patient The overall probability of developing lung cancer, which cannot distinguish the relevant disease variables of developing lung cancer, often requires a doctor's assessment

The traditional model is mainly based on epidemiological survey data, such as age, smoking status and occupation, etc., and information bias is inevitable in the information collection process; and, although there are many sources of medical data, the data forms are diverse.

There are multiple hospitals, outpatient clinics and other medical institutions in each region, and each medical institution has its own habit of recording diseases, resulting in messy data, screening and combing data related to lung cancer is a heavy workload, and it is easy to miss, lacking effective data normalization method

Images