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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".