RESUMO
Background: Minimal hepatic encephalopathy [MHE] is the mildest type of hepatic encephalopathy in patients with cirrhosis. Patients with MHE have normal clinical and physical examination but they show some neurocognitive dysfunctions that affect their quality of life negatively. The aim of the current study is to diagnose MHE in patients with cirrhosis and its associated factors
Methods: This is a cross-sectional study on 120 known cases of cirrhosis referred to hospitals affiliated to Isfahan University of Medical Sciences during 2014-17. The patients' cirrhosis severity was evaluated using laboratory tests and physical examinations based on MELD [Model for End-stage Liver Disease] and Child-Pugh criteria. The patients' demographics were filled in a checklist. All included patients with cirrhosis were asked to respond to the questions of Psychometric Hepatic Encephalopathy Score [PHES] test
Results: Mean age of the patients was 51.2 +/- 9.7 years. 62 [51.7%] patients were men and 58 [48.3%] patients were women. The mean score of the patients based on MELD criteria was 14.03 +/- 6.09. 26.7% of the patients presented MHE. Mean age of the patients with MHE was statistically less than the patients without MHE [p value < 0.001]. Mean score of MELD criteria among the patients with diagnosis of MHE was significantly higher than the other group [p value < 0.001]. The patients' Child class was statistically associated with MHE [p value < 0.001]. Men were significantly more affected than women [p value = 0.03]
Conclusion: MHE was associated with MELD score and Child class of the patients with cirrhosis. The noticeable point was reversible association of age with MHE. Further studies are recommended
RESUMO
Introduction: With the extensive use of electronic medical records systems, a large amount of medical text data is produced daily in the hospitals and other medical environments that organizing this text information is important and necessary. Also, a need to automatically retrieve useful knowledge from this data to help clinicians is felt. In order to extract the hidden values in the medical text documents, text mining can be used in the field of health
Methods: In this review study, SID, Magiran, Pubmed, ScienceDirect, IEEE, and Google Scholar databases were searched with the keywords including [Text Mining] AND [Medicine]¡ [Clinical Text Mining] AND [Predict]¡, [knowledge discovery in medical text] and [Text Mining for Medical and Healthcare] in the English databases and keywords such as [text mining] AND [Discovering Knowledge in Medicine] in the Persian databases. Then, all articles that somehow refer to Medical knowledge discovery and text mining applications in the field of health were selected
Results: Text mining is one of the important and powerful techniques for extracting information from health information systems. Text mining in clinical data provides potential for new discoveries and it also improves efficiency and communication in hospital systems for doctors and hospital administrators
Conclusion: Nowadays, text mining in clinical documentation, is one of the developed technologies for the discovery of medical knowledge that its use in medical databases is essential to achieving immediate access to important health resources, and its application can improve patient care and reduce medical costs