Welcome to DocBot, an intelligent conversational agent designed to excel in recognizing and addressing medical inquiries within its specialized domain. Trained on a diverse dataset encompassing diseases like cancer, diabetes, and heart attack, DocBot is tailored to provide valuable insights and engaging responses in the field of health.
DocBot's brain is powered by a neural network implemented using PyTorch. The model architecture comprises three layers:
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Input Layer: The initial layer processes the input data.
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Hidden Layers: Two hidden layers follow, enhancing the model's ability to capture intricate patterns.
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Output Layer: The final layer produces the model's predictions.
The ReLU activation function is applied between layers to introduce non-linearity, crucial for the model's learning capabilities.
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Specialized Scope: DocBot excels in addressing medical inquiries, demonstrating proficiency in specific health topics.
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Continuous Updates: The model undergoes continuous updates to ensure adaptability and relevance in the dynamic field of healthcare.
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Enriched User Experience: DocBot is committed to providing a consistently enriched and positive user experience, contributing to a reliable source of medical information.
For transparency and accountability, the dataset used to train DocBot is accessible here.
Experience DocBot in action through the Live App. Feel free to engage with DocBot for medical inquiries and explore its capabilities.