Intelligent Depression Pattern Identification Using Artificial Intelligence in the Patient Health Records
DOI:
https://doi.org/10.70445/avjcs.2.1.2025.37-47Keywords:
AI, Sentiment Analysis, Depression, Machine Learning, Healthcare, NLP, Early Diagnosis, Patient Records, Mental Health, Predictive Modeling, Depression DetectionAbstract
Studies show that mental health conditions especially depression create more disability problems worldwide. The traditional ways doctors check for depression depend too heavily on what medical staff ask and what patients say which results in missed or wrong depression findings. Technology tools that simulate human intelligence help us better diagnose mental health problems and detect them earlier by reading large healthcare datasets. This research joins Artificial Intelligence systems with advanced language processing tools to spot depression signs within patient healthcare files. Our research shows that researching unstructured medical data elements like doctor notes and therapy conversations helps find emotional responses. By reading medical records through sentiment analysis and machine learning AI helps doctor’s spot undetected depressive states. This study looks at how AI systems work in mental health care today while discussing the problems and benefits of applying this technology.
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