tom arnold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）
In recent years, the prevalence of mental illness has been increasing, posing a great threat to people's lives and health. Therefore, it is particularly important to study how to predict and diagno mental illness. This study aims to explore a ChatGPT-bad mental dia risk prediction model, through training a large amount of mental health data, to achieve accurate prediction of patients' mental dia risk.
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I. Introduction
password 什么意思With the development and progress of society, the pace of people's life is accelerating, the pressure of competition is increasing, and the incidence of mental illness is increasing year by year. Mental illness not only brings a huge burden to patients and families, but also caus immeasurable loss to the whole society. Therefore, it is particularly important to study how to predict and diagno mental illness.
Traditional mental health asssment is mainly bad on questionnaires and clinical diagnosis, which has the disadvantages of high time and energy costs, and the asssment results are easily affected by individual subjective factors. With the continuous development of artificial intelligence technology, mental dia prediction models bad on technologies such as machine learning and natural langua
ge processing have gradually become a rearch hotspot.
trouble maker是什么意思2. ChatGPT-bad mental dia risk prediction model
pyp>brazil1. Rearch Background
The occurrence of mental illness is related to many factors, including genes, environment, lifestyle and so on. Traditional mental health asssment is mainly bad on questionnaires and clinical diagnosis, which has the disadvantages of high time and energy costs, and the asssment results are easily affected by individual subjective factors. Therefore, it is of great significance to develop a mental dia risk prediction model bad on natural language processing technology.
addoil2. Data collection and processing
The data t ud in this study is the mental health data from the psychological counling platform, including the patient's personal information, psychological condition, lifestyle, etc. In order to protect data privacy, we have densitized the data and removed nsitive information such as name, ID number, etc.
In the data preprocessing stage, we ud a variety of techniques to clean and transform the data. Fir
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st, we gmented the text data and converted each word into a vector reprentation. We then normalized the vectors to ensure that values from different features have the same weight. Finally, we split all the procesd data into training, validation and test ts.
玷污什么意思3. Model design
The prediction model ud in this study is an improved version bad on the ChatGPT model. The ChatGPT model is a pre-trained model bad on the Transformer structure and has excellent natural language processing capabilities. Bad on the ChatGPT model, this study optimized the model structure and training method to improve the prediction performance of the model.
Specifically, we design a neural network model bad on multi-task learning, including two tasks: ntiment classification task and ntiment regression task. The emotion classification task is to classify text into three emotional categories of positive, neutral, and negative, and the emotion regression task is to predict the verity of a patient's mental illness. We trained the model in an alternate training manner, that is, training the emotion classification task first, and then training the emotion regression task.
4. Model evaluation
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To evaluate the predictive performance of the model, we ud multiple metrics including precision, recall, F1 score, etc. At the same time, we also adopted techniques such as cross-validation and hold-out method to ensure the stability and generalization performance of the model. In the end, we obtained excellent prediction results on the test t, which proved the effectiveness and feasibility of the ChatGPT-bad mental dia risk prediction model propod in this study.
