Deep learning Based Patient-Friendly Clinical Expert Recommendation Framework
Bibliographic record
Abstract
In recent years, with the popularization of the Internet and the development of technologies such as big data analysis, people's demand for mobile medical services has become more and more urgent, which is manifested in determining their diseases based on symptoms and selecting hospitals with better service quality according to the illnesses and doctors. An inquiry recommendation system is designed and implemented based on knowledge graphs and deep learning technology to solve the above problems. Based on the open medical data on the Internet, a “disease-symptom” knowledge map is constructed to help users self-examine according to symptoms. The knowledge map embedding model trains the embedded vector representation of entities in the knowledge map. The most similar is selected according to the Euclidean distance similarity of the vector. The disease entity enriches recommendation options, and the two are combined to achieve disease diagnosis services. At the same time, based on social media comment data, combined with the existing medical service quality evaluation indicators, the deep learning analysis method is used to automatically give a multi-dimensional score of the doctor's service quality and provide users with the doctor and hospital recommendation services. Finally, by constructing test sets and designing questionnaires, it is verified that the accuracy rates of disease diagnosis service and doctor-hospital recommendation service are 74.00% and 90.91 %, respectively.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".