AI Chaperone: Awareness Chatbot for Alzheimer’s Disease
Bibliographic record
Abstract
This project proposes the development of a RAG Chatbot tailored specifically to assist Alzheimer's patients in managing their memory abnormalities and providing them with answers to their queries. The chatbot utilizes state-of-the-art natural language processing techniques to understand and respond to patient inquiries, while leveraging RAG to enhance the relevance and accuracy of its responses.The primary goal of this project is to provide Alzheimer's patients with a supportive and interactive tool that can help mitigate the impact of memory impairment on their daily lives. It acts as an information resource, offering answers to common questions about Alzheimer's disease, treatment options, and lifestyle management strategies.Key features of the RAG Chatbot include personalized conversation histories to track patient interactions and preferences, adaptive dialogue generation to tailor responses to individual needs, and integration with existing healthcare systems for seamless coordination of care. Furthermore, the chatbot undergoes continuous improvement through machine learning algorithms that analyze patient feedback and update its knowledge base accordingly. Overall, the RAG Chatbot represents a promising advancement in Alzheimer's patient assistance, offering a scalable and accessible solution to support individuals living with memory disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".