Method for early identification of bipolar disorder based on BDNF

A technology for early identification of bipolar disorder, applied in the field of clinical psychiatric detection, can solve problems such as distance and inconsistent results, and achieve the effects of improving work efficiency, improving prognosis, and shortening the time of diagnosis and treatment

Pending Publication Date: 2020-06-09
SHANGHAI MENTAL HEALTH CENT (SHANGHAI PSYCHOLOGICAL COUNSELLING TRAINING CENT)
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Problems solved by technology

[0004] At present, research on biomarkers for early diagnosis of bipolar disorder mainly focuses on basic research such as hematolog

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  • Method for early identification of bipolar disorder based on BDNF
  • Method for early identification of bipolar disorder based on BDNF
  • Method for early identification of bipolar disorder based on BDNF

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings, but not as a limitation of the present invention.

[0046] see Figure 1-3 , the present invention has designed a kind of method based on BDNF early identification bipolar disorder, carries out as follows:

[0047] S1 data acquisition module 1 acquires detection data sets including age, plasma mRNA level, BDNF level, mBDNF level, proBDNF level and other detection data sets from the outside, wherein the plasma mRNA level, BDNF level, mBDNF level, and proBDNF level are the patients after sampling the depressive episode. The inspection structure for cross-sectional data;

[0048]The S2 data extraction module classifies the detection data set into a prediction model data set and an auxiliary model data set, wherein the prediction model data set includes age, BDNF level, plasma mRNA level; the auxiliary model data set includes age, mBDNF level, proBDNF level ;

[0049] S3 ...

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Abstract

The invention relates to the technical field of clinical medicine psychosis detection, in particular to a method for early recognition of bipolar disorder based on BDNF. A data acquisition module acquires a detection data set; a data extraction module divides the data set into a prediction model data set and an auxiliary model data set; a bipolar disorder onset risk prediction model is used for predicting a model data set and obtaining a bipolar disorder onset risk prediction probability value; a bipolar disorder auxiliary diagnosis model processes the auxiliary model data set to obtain a bipolar disorder auxiliary diagnosis probability value; the probability that the patient suffers from the bipolar disorder can be clearly seen through the two probability values, misdiagnosis is greatly avoided, and a doctor can determine a proper treatment scheme in a short time by using the method, so that the diagnosis and treatment time is shortened, and the working efficiency and the accuracy ofthe diagnosis and treatment scheme are improved; meanwhile, as a patient or a family member of the patient, self-judgment can be performed on the patient or the family member of the patient, so that prognosis of the disease is improved.

Description

technical field [0001] The invention relates to the technical field of clinical psychiatric detection, in particular to a method for early identification of bipolar disorder based on BDNF. Background technique [0002] Mental disorders are central nervous system dysfunctions caused by brain activity disorders, and clinical manifestations are abnormalities in cognition, emotion, will, and behavior. With the development of society and the increasing competition mechanism, all kinds of mental health problems will become more prominent. Mental illness has become a serious and costly global public health problem, affecting people of all ages, cultures, and socioeconomic status. [0003] Bipolar disorder is a relatively common and serious severe mental illness characterized by high and low mood swings and chronic recurrent episodes. The mood states of patients with bipolar disorder mainly include (hypo)manic episodes, depressive episodes, mixed state episodes, cycloaffective epi...

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Application Information

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IPC IPC(8): G16H50/20G16H20/70G16H50/70
CPCG16H50/20G16H20/70G16H50/70
Inventor 方贻儒李则挚赵国庆汪作为张晨
Owner SHANGHAI MENTAL HEALTH CENT (SHANGHAI PSYCHOLOGICAL COUNSELLING TRAINING CENT)
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