Computer Technology to Improve Medical Information in Bangkok, Thailand
Bibliographic record
Abstract
Building hospital applications based on services allow hospitals and other organizations to cooperate and make use of business functions. Hospital information systems that involve extensive information exchange across hospitals and organization boundaries, such as patient profiles, can easily be automated. Service-based applications can be constructed by linking services from various providers using either a standard programming language or a specialized workflow language. This paper reviews the use of computer technology which supports health services in Bangkok, Thailand by developing a survey of health services in hospitals, in which the focus is on the attitudes and competence of medical students and physicians, and the availability of health services equipment; and analyzing and providing guidance via a web service that supports health services. A prototype of a web application is created to simulate situations of the use of computer-based devices for supporting clinical operations, involving 12 medical doctors and 3 patients. Two cases are analysed to demonstrate different situations of the web service. In such situations, stakeholders are requested to query patient information and specify the documents. The experiments have been evaluated by considering straightforward criteria to perform activities with the prototype to determine how accurate the documents are requested and specified, and evaluate how the health service performs efficiently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".