基于ChatGPT的心理疾病风险预测模型研究（英文中文双语版优质文档）
python官方文档中文版ChatGPT model has demonstrated its powerful predictive ability in natural language processing. This research aims to explore the risk prediction model of mental illness based on the ChatGPT model, and provide new ideas and methods for clinical practice.
1. Research Background
With changing lifestyles and increased psychological stress, mental illness has become a serious problem worldwide. Most of the existing mental disease risk assessment tools are based on questionnaires and interviews, which have problems of insufficient subjectivity and objectivity. The mental disease risk prediction model based on natural language processing technology can achieve more objective and accurate risk assessment through the analysis of patients' daily language.
2. Research Methods
This study adopts the ChatGPT-based mental disease risk prediction model, the specific method is as follows:
1. Data Collection
In order to build a mental disease risk prediction model, we need to collect a large amount of corpus data. We collected multiple corpora in the field of psychology, including mental health websites, patient texts provided by counselors, social media, and other sources. These texts contain a variety of information about mental illness, including the patient's symptoms, emotions, behavior, and more.
2. Data preprocessing
We use the Python language for data preprocessing, including word segmentation, stop word removal, and noise removal, to ensure the quality and accuracy of the data. We used tools such as the open source Chinese word segmentation tool jieba, stopwords removal tool stopwords, and regular expressions for cleaning text for data processing.
3. Model pre-training
We used the ChatGPT model based on the PyTorch framework to pre-train the mental illness corpus. We adopted a 12-layer Transformer model structure with a training dataset size of 120 million. In order to improve the performance of the model, we use the Adam optimizer and adopt the technique of learning rate adjustment. In the process of pre-training, the tasks of mask language model prediction and next sentence prediction are used.
4. Model fine-tuning
After completing the pre-training, we use the ChatGPT model in the task of predicting the risk of mental illness. We fine-tuned the model using labeled datasets, which include classification information of mental illnesses, such as depression, anxiety, and obsessive-compulsive disorder. We fine-tuned the model for 10 epochs and used the cross-entropy loss function for model optimization.
5. Model Evaluation
In order to evaluate the performance of the model, we use three indicators of accuracy, recall and F1-score for evaluation. We divide the dataset into training set and test set, where the training set accounts for 70% and the test set accounts for 30%. After evaluation, the precision, recall and F1-score of our model on the test set are 87.2%, 86.5% and 86.8%, respectively.
3. Research results
Our study shows that the ChatGPT-based mental illness risk prediction model has high prediction accuracy and reliability. Compared with traditional mental disease risk assessment tools, our model can more objectively and accurately assess patients' psychological conditions, which will help improve clinical diagnostic accuracy and treatment effects.
In addition, our model has the following advantages:
1. The model is based on natural language processing technology, which avoids the problems of insufficient subjectivity and objectivity of traditional mental disease risk assessment tools.

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