Automatic stroke detection method

Abstract

The invention discloses an automatic stroke detection method which comprises the following steps: acquiring a pathological data set of a stroke patient, the pathological data set comprising a plurality of pathological parameters associated with the patient; screening parameters significantly related to stroke recurrence from the pathological data set of each stroke patient, and determining the parameters as a pathological data subset; updating the pathological data subset of the stroke patient on the basis of hospitalization registration records, physical examination, outpatient reexaminationand the like of the stroke patient; and performing comparative analysis on the pathological data subset of each stroke patient and a recurrence prediction model in real time to obtain a correspondingdanger level, and informing the stroke patient for subsequent visit in time based on the danger level. The method has the advantages and effects that the risk coefficient of stroke of the patient canbe predicted, and the patient can timely go back for repeated diagnosis and treatment.

Description

technical field [0001] The invention relates to the technical field of health management, in particular to an automatic detection method for the onset of cerebral apoplexy. Background technique [0002] Stroke is the third leading cause of death worldwide and the leading cause of adult disability. It is also one of the important causes of cognitive impairment and emotional impairment in the elderly. my country is a country with a high incidence of cerebrovascular diseases, and two-thirds of stroke patients die or leave disabilities of varying degrees, causing a huge economic burden to the country and families. [0003] Stroke not only has high morbidity, high mortality, and high disability rate, but also has a high recurrence rate. The mortality rate caused by stroke recurrence is much higher than the mortality rate of new stroke. [0004] In the prior art, it is often based on the clinical manifestations of the patient to determine whether the patient has the disease, so i...

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Problems solved by technology

[0004] In the prior art, it is often based on the clinical manifestations of the patient to determine whether the patient has the disease, so it is easy to miss the best opportunity to prevent stroke. Therefore, the recurrence of stroke can be effectively predicted, so that measures can be taken to prevent the recurrence of stroke becomes more important

[0005] However, in the prior art, the recurrence of stroke patients cannot be predicted, resulting in the inability to treat and prevent in time

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