Advanced Medical Chatbot based on named data networking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of a chatbot is to make human and machine interaction possible. In this scenario, the chatbot is a medical consultant between the patients and the doctors. For this purpose, there is a need of the Human-Computer Interaction(HCI) research which includes study and implementation intended for human use. So we design a system that uses Artificial Intelligence Markup Language(AIML) and Natural Language Processing (NLP). And for the purpose of communication needs, we use Name Data Networking(NDN) instead of the typical IP protocol. NDN provides two essential properties which are its Consumer Driven Nature in Communication and the privacy it provides. The motivation behind the idea is that much of the needed answers are available on the internet about the medication which the chatbot provides in a friendly manner and if there are serious issues provide consultations as well as appointments with the Doctor as early as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it