Triage-Bot: An Assistive Triage Framework
Bibliographic record
Abstract
Prompt delivery of healthcare services is critical for saving lives. Streamlining the collection of patients’ health data through automation can minimize waiting times in emergency departments and enhance access to evidence-based care. We propose an innovative solution, Triage-Bot, which leverages the latest advancements in Artificial Intelligence to elevate the standard of patient care. Triage-Bot is a software as-a-service that allows patients or their caregivers to authenticate themselves by presenting their faces to the camera and also using a registered user ID and password. They can communicate about their health situation through voice, chat, and video conversations securely with the robot service to request medical attention. Moreover, Triage-Bot automatically captures vital signs from facial video. Voice data is converted to text to extract a concise summary of the patient’s health condition, while emotions are detected from both text and video data. The processed patient information is linked to the patients’ Electronic Medical Records to automatically classify the patients into one of the five severity level as defined by the Canadian Triage and Acuity Scale. Currently, we are working on the evaluation of the services by medical professionals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".