SISTEM PENDUKUNG KEPUTUSAN KLINIS UNTUK MENENTUKAN JENIS GANGGUAN PSIKOLOGI PADA PASIEN GAGAL GINJAL KRONIS (GGK) YANG MENJALANI TERAPI HEMODIALISA
| Main Authors: | Rosmalia, Lia, Kusumadewi, Sri |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Prodi Teknik Informatika Universitas Sahid Surakarta
, 2018
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| Online Access: |
http://jurnal.usahidsolo.ac.id/index.php/GI/article/view/268 http://jurnal.usahidsolo.ac.id/index.php/GI/article/view/268/222 |
Daftar Isi:
- Psychological factors in patients with chronic renal failure conditions are severely affected by prolonged course of the disease, disability and discomfort should depend onthe hemodialysis machine. Hemodialysis therapy in addition to disturbing the physical, complications can trigger mental disorders. Patients with chronic renal failure often have psychological disorders associated with general medical conditions. Anxiety and depression are often psychological disorders experienced. Symptoms that are almost similar to each other will require experts to correctly identify them based on the patient's perceived symptoms. Limitations of health practitioners in exploring thepsychological conditions felt by the patient to be one reason for the necessity of a clinical decision support system capable of integrating patient information (demographic, clinical, social psychological) with a knowledge base with the aim of identifying psychological conditions in a clinical setting that can assist physicians, nurses, psychologists and health practitioners others in making a clinical decision on their patients. The approach in this study uses case-based reasoning (Case-Based Reasoning) (CBR). CBR process through four stages of the process are: retrieve, reuse, revise and retain. If there is a similar case then the reasoning to weigh the nearest case using the Simple Matching Coefficient (SMC) method so that the system is able to streamline the diagnostic process by taking into account the closeness between the base case and the target case. As a result, the system will be able to provide a recommendation picture of the initial diagnosis of the highest percentage of possible types of psychological disturbance suffered with its severity level and the best solution for new cases based on the nearest case solution of the nearest similarity level.