SAPIAN-VTHS (Virtual Teaching Hospital System) Electives networking project
Bibliographic record
Abstract
This report provides a detailed account of Adwoa Donyina’s development in the SAPIAN-VTHS (Virtual Teaching Hospital System) Electives networking Software development project. The main significant contribution to this software system is the re-implementation of an expert system into an artificial neural network system. \n \nVarious critical design decisions were involved in the creation of the VTHS Neural Network. The main challenges presented in this project were due to the fact that \nArtificial Neural Networks is new and emerging technology; hence the system used the backpropagation algorithm which is one of the relatively new neural network methods. \n \n“Backpropagation, or propagation of error, is a common method of teaching artificial neural networks how to perform a given task. It was first described by Paul Werbos in 1974, but it wasn't until 1986, through the work of David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams, that it gained recognition, and it led to a “renaissance” in the field of artificial neural \nnetwork research.” \n \nVarious advanced concepts used in the development were taught to Adwoa in her CSC321 - Neural Networks undergraduate module at University of Toronto by Prof. \nGeoffery E. Hinton, who is “most noted for his work on the mathematics and applications of neural networks.” such as the backprogation algorithm, as stated in the above quote. \n \nThe aim of this project is to assist in teaching medical students the pedagogical thinking process, by modelling the business process. The objective of this project is to \nre-implement the diagnosis components of a working web- based diagnostic teaching system as an artificial neural network. This project is demanding because it involves \nre-engineering someone else’s code, and is in a non-familiar medical domain.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".