Intelligent diagnosis-and-treatment auxiliary system for blood disease

Abstract

The invention provides an intelligent diagnosis-and-treatment auxiliary system for a blood disease. The system comprises the components of a patient basic information module which is used for storinghospitalizing information, pathogenesis records, doctor's advice information, hospital leaving records, clinic information and a body temperature table of the patient; a routine examination module which is used for storing all routine examination receipts and classifying the receipts, wherein the receipts in each type are arranged in a time sequence, thereby realizing retrieval in a list or distribution map manner; a blood disease examination module which is used for storing the blood disease examination receipts of the patient; and a diagnosis-and-treatment process module which is used for calling related indexes in the patient related information module, the routine examination module and the blood disease examination module according to a corresponding blood disease classifying algorithm, thereby calculating a corresponding blood disease type and a prognosis risk degree grade; calls all matched treatment solutions from a treatment plan database according to the blood disease type and the prognosis risk degree grade and performs displaying.

Description

technical field [0001] The disclosure belongs to the field of artificial intelligence, and in particular relates to an intelligent diagnosis and treatment assistance system for blood diseases. 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] Hematological diseases are also known as hematopoietic system diseases, and the etiology of malignant hematological diseases such as leukemia, lymphoma, and multiple myeloma is very complicated. Hematological diseases are characterized by diverse clinical manifestations, complicated laboratory examinations, complex diagnosis and classification, detailed prognosis stratification, periodic treatment, long follow-up time, strong professionalism, and rapid diagnosis and treatment updates. For example, acute myeloid leukemia can be divided into nearly 20 subtypes according to the WHO classification; labora...

AI Technical Summary

Problems solved by technology

Doctors need to make summary judgments on the basis of gathering various types of knowledge and data. On the other hand, medical data in most hospitals are distributed in multiple systems such as HIS and LIS, forming an information island and lacking unified operation and summary for the diagnosis and treatment process. From the view, all kinds of diagnosis and treatment knowledge and norms are also relatively complicated and scattered, which makes clinicians spend a lot of time and energy in searching for cases, reviewing medical history, clarifying treatment process, and formulating the best treatment plan, etc., resulting in heavy workload and Increased duplication of labor, unable to accurately recommend a matching treatment plan

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