Expert System To Determine Psychological Disorders In Chronic Kidney Failure (CKD) Patients Undergoing Hemodialysis Therapy Using Certainty Factor Method
Why this work is in the frame
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Bibliographic record
Abstract
Chronic Kidney Failure (CKD) is damage to the kidneys both in structure and/or function that lasts for 3 months or more. Hemodialysis is a prolonged therapy that can significantly impact the physical and psychological well-being of patients with chronic kidney disease. This therapy has a big effect on sufferers. The psychological impact that appears can affect the success of therapy so it is important to recognize these symptoms and provide appropriate treatment to overcome them. Based on research at Delia General Hospital, patients who will undergo Hemodialysis therapy must come to the hospital to receive comprehensive therapy by a doctor. Long patient queues when undergoing therapy can make patients tired and remember the patient's condition in order to get information and therapy. Handling of these problems can be overcome by building a system that can determine psychological disorders in patients. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques in solving problems that usually can only be solved by an expert in a particular field. Certainty Factor (CF) is a method capable of defining the degree of certainty of a rule or fact in describing an expert's belief in the problem at hand. With an expert system, it can help identify and determine early on psychological disorders in patients. From the results of trials conducted by expert systems to determine psychological disorders in patients with kidney failure using the Certainty Factor method, the highest value is depression with a percentage of 94.59%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it