Abstract: The application belongs to the field of big data, and particularly relates to an official document processing method, device, computer equipment and storage medium. The method includes the following steps of: performing format analysis on the to-be-reviewed official document, then acquiring the to-be-reviewed official document of standard file type, and identifying all file components and contents in the to-be-reviewed official document of standard file type; performing text format detection, text content detection and frame layout detection synchronously by a preset text processing model, obtaining a format detection result, a content detection result and a layout detection result; generating a detected error content according to the format detection result, content detection result and layout detection result, calling out a standard writing rule corresponding to the detected error content, marking the detected error content and the standard writing rule in the to-be-reviewed official document.

Abstract: The application belongs to the field of big data, and particularly relates to an official document processing method, device, computer equipment and storage medium. The method includes the following steps of: performing format analysis on the to-be-reviewed official document, then acquiring the to-be-reviewed official document of standard file type, and identifying all file components and contents in the to-be-reviewed official document of standard file type; performing text format detection, text content detection and frame layout detection synchronously by a preset text processing model, obtaining a format detection result, a content detection result and a layout detection result; generating a detected error content according to the format detection result, content detection result and layout detection result, calling out a standard writing rule corresponding to the detected error content, marking the detected error content and the standard writing rule in the to-be-reviewed official document.

Abstract: An incidence rate monitoring method, apparatus and device based on historical disease information, and a computer-readable storage medium, wherein the incidence rate monitoring method based on historical disease information includes: forming a prediction model of incidence rate monitoring based on historical disease information through continuous and autonomous learning of historical medical record data based on a combination of a preset gated recurrent neural network and an ensemble learning algorithm, and then inputting disease data based on the to-be-predicted disease into the prediction model for prediction and monitoring. The prediction model is formed by capturing a certain pattern from the historical medical record data through the combination of the above-mentioned algorithm and neural network.

Abstract: A machine learning based medical data classification method is provided. The method includes: a medical data classification request including medical record information is received; a preset medical term base is obtained, and word segmentation is performed on the medical record information according to medical terms in the medical term base to obtain multiple text vectors; features of the multiple text vectors are extracted to obtain multiple text vectors and corresponding feature dimension values; a target classifier is trained with multiple pieces of medical data, and the multiple text vectors and the corresponding feature dimension values are traversed and calculated; until a target node corresponding to the multiple text vectors is traversed, class probabilities corresponding to the multiple text vectors are calculated according to the target node, and a class result corresponding to the medical record information is obtained according to the class probabilities and is pushed to a terminal.

Abstract: The application discloses an intelligent data analysis method and device, a computer device, and a storage medium. The intelligent data analysis method includes that: a public opinion factor obtained and a public opinion index carrying a time label are taken as first portrait data (S40); original sample data is obtained based on the first portrait data and medical data; the original sample data is cleaned to obtain sample data to be processed (S50); lag processing is performed on the sample data to be processed to obtain lag sample data (S60); feature expansion is performed on the lag sample data to obtain target sample data (S70); and an improved multi-granularity cascading random forest algorithm is used to train the target sample data to obtain a target forecast model (S80); the improved multi-granularity cascading random forest algorithm includes a pooling layer, which is used for retaining data features (S90).

Abstract: Disclosed are a social security fraud behaviors identification method, a social security fraud behaviors identification device and a social security fraud behaviors identification apparatus as well as a computer-readable storage medium. The method includes: establishing a relationship network of doctor-patient and drug diagnosis based on social security medical treatment data, wherein, the relationship network comprises kinds of nodes, the relationship between each node and any other node is different; analyzing group medical treatment behaviors of each node in the relationship network, to extract multiple-dimensional group medical treatment characteristics corresponding to each node; inputting each of the multiple-dimensional group medical treatment characteristics extracted into a preset classification model, to identify fraud rate of each node according to the classification model.