Pengelompokan Data Rekam Medis pada Pasien Penyakit dalam Untuk Meningkatkan Manajemen Informasi Kesehatan Berdasarkan Wilayah Kota Binjai Menggunakan Algoritma Clustering K- Means
Bibliographic record
Abstract
The history of disease in patients is generally recorded in medical record data in every hospital as well as at Artha Medika Hospital which is a health institution that was established in 2012 in the city of Binjai also has a very large amount of medical record data. However, in using the information management system owned by Artha Medika Hospital, there are weaknesses and it is still limited in managing medical record data in the hospital which is used in making reports to the head of the leadership. Therefore, a system is needed that can assist the hospital in improving health information management to be faster in managing data by approaching using data mining techniques with the k-means method. So that in finding new information based on medical record data of internal medicine patients can be used in the decision-making process by hospital management to be right on target so that it can produce 3 groups of data consisting of Age, Type of disease and Region. From testing on cluster 3, it can be seen that the results of the age group (X), type of disease (Y), region (Z) the amount of data owned is 645 cluster 3 data centred on the centroid of the information of the number of patient medical records data, namely age is 44-52 years, with the type of disease is chronic kidney disease and the region is South Binjai.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".