IEEE Journal of Biomedical and Health Informatics · 2023 · 30 citations · 0 references
ComputingComputer ScienceHealth InformaticsArtificial IntelligenceMachine LearningNatural Language ProcessingDeep LearningMental HealthMental IllnessNeural NetworksPattern RecognitionRepresentation LearningDeep Learning For Health MonitoringExplainable Artificial IntelligenceMachine Learning Detection ModelsMachine Learning For Health MonitoringMachine Learning For HealthcareMachine Learning Prediction ModelsReal-time Monitoring
This paper introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. This approach leverages GPT embeddings and the fine-tuning of GPT-3. This approach exhibits superior performance in both classifying mental health disorders and generating explanations with accuracy of around 87% in classification and Rouge-L of around 0.75. We utilized GPT embeddings with machine learning models for the classification of mental health disorders. Additionally, GPT-3 was fine-tuned for generating explanations related to the predictions made by these machine learning models. Notably, the proposed algorithm proves well-suited for real-time monitoring of mental health by deploying in AI-IoMT devices, as it has demonstrated greater reliability when compared to traditional algorithms.