An Exploratory Study on the Prediction of Recurrence Risk of Bipolar Disorder Using Sentiment Analysis Technology Based on Multi-modal Feature Fusion

An Exploratory Study on the Prediction of Recurrence Risk of Bipolar Disorder Using Sentiment Analysis Technology Based on Multi-modal Feature Fusion

Bipolar disorder (BD) has become a significant public health problem with complex clinical manifestations, difficult treatment, and poor prognosis. However, there is still a lack of effective biological markers for diagnosing and predicting recurrence. Sentiment analysis computing usually refers to using machine equipment to classify, identify, interpret, and imitate human emotions. However, current multi-modal emotion analysis research is mainly based on one or two modalities. Due to the diversity and complexity of patients' emotional expressions, this single- and dual-modal information analysis is far from enough for accurate discrimination of emotional symptoms. Only emotion analysis technology based on multi-modal feature fusion can make more precise and effective judgments. The current project is based on our previous research on cognitive neuroimaging and big data analysis of bipolar disorder. The investigators plan to enroll 200 BD patients who meet DSM-5 diagnostic criteria and 200 healthy controls. The investigators will use sentiment analysis technology with multi-modal feature fusion (text data, audio and visual modalities, eye movements, and electrophysiology) to identify BD recurrence. Biological markers for risk prediction and an algorithm model for joint judgment of multi-source information will be established to analyze the characterization data. The effectiveness of this recurrence prediction model will be further verified and optimized through a large-sample, prospective cohort study design. It is hoped that it can provide a new method for predicting the recurrence risk of BD patients. In the near future, clinical decision-making aids based on this auxiliary method can be developed, and the translational application value of clinical diagnosis and treatment can be explored.

Patients group inclusion criteria:

• Patients who have previously met or currently meet the diagnostic criteria for bipolar disorder according to DSM-5, whose condition and treatment are currently stable, and who cooperate with the assessment;
• Age ≥18 years, <65 years;
• Han Chinese ethnicity;
• Sufficient visual and auditory abilities to complete the necessary examinations for the study;
• Understanding the study content and signing the informed consent form. If the patient is unable to sign the informed consent form personally due to low education level or other reasons, it may be signed by a relative or guardian on their behalf.

exclusion criteria:

• The presence of intellectual disability or other conditions that significantly affect the patient's current mental state;
• the patient has a serious or unstable physical illness, including: neurological disorders (delirium, dementia, stroke, epilepsy, migraine, etc.), congestive heart failure, angina pectoris, myocardial infarction, arrhythmia, hypertension (including untreated or uncontrolled hypertension), malignant tumors, immunodeficiency, and blood glucose levels higher than 12 mmol/L; or other diseases that may interfere with the test assessment (abnormal indicators more than twice the normal value).

Healthy controls group inclusion criteria:

• Age ≥ 18 years, < 65 years;
• Han Chinese ethnicity;
• Gender matched to the patient group;
• Sufficient visual and auditory ability to complete the necessary examinations for the study;
• Understanding of the study content and signing of informed consent;
• No family history of mental illness. exclusion criteria:
• Individuals with a mental disorder conforming to DSM-5, or those with suspicious mental symptoms but not meeting the diagnostic criteria;
• Individuals with severe physical illness that makes it difficult to complete the necessary examinations.