Grouping Patient Data Based On Work And Place Of Residence On Perceived Complaints
Bibliographic record
Abstract
Every day the Sawit Seberang Health Center serves many patients with various kinds of disease complaints from various areas in Sawit Seberang District. The number of patients can even reach tens of people in one day resulting in a large number of patient visit data. Limited information regarding the spread of diseases that are often suffered by patients in several areas at the Sawit Seberang Health Center has resulted in less optimal policy action, anticipation of treatment and prevention of disease in the community. To find information about grouping patient data based on work and place of residence for perceived complaints, a large or large data mining technique is needed, namely data mining techniques using the clustering method. The purpose of this study is to process and cluster patient data based on work, place of residence and complaints that are felt using the Clustering method, to analyze the results of applying data mining using K-Means Clustering in grouping patient data based on work, place of residence and complaints that are felt and find out the results of the settlement grouping patient data based on work and place of residence on perceived complaints using clustering and data mining methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".