Intelligent Multi-Purpose Healthcare Bot Facilitating Shared Decision Making
Bibliographic record
Abstract
Patient decision aids (PtDAs) have been promoted to facilitate personalized information retrieval and decision support; nonetheless, although promoted for more than 20 years, they have generally failed to gain a foothold in the general delivery of healthcare. Intelligent interactive agent technologies could address the design features necessary to facilitate support and shared-decision making. In this thesis, we develop and build a PtDA for Prostate cancer using intelligent agent technology. The proposed system, called ALAN, has a multi-layered architecture with three layers. While the first layer (User-Interface) is responsible to effectively interact with users (patients and physicians), the bottom layer (Data) handles requests regarding storing and retrieving the data. Unlike most existing bots, our core objective is to enable ALAN with learning abilities, which can evolve in the course of time and improve its behaviour with minimum distraction of the user. To this end, reinforcement learning and deep learning algorithms are employed in the main layer, i.e., Analytical Decision Making. This research is expected to have impact on delivery of personalized healthcare.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".