健康・医療情報学ジャーナル

Abstract Introduction: Tobacco smoking, a habitual behavior, is addictive and detrimental to health. Quitting requires personal abilities and environmental opportunities and therefore, improving these abilities and opportunities will undoubtedly act on smokers’ motivation to quit. Methods: A prospective single-blinded randomized controlled interventional study was conducted among first year undergraduate students in Malaysia. A total of eighty smokers were randomly allocated to a control or intervention groups (40/40). Randomization remained concealed from research personnel. All participants were followed up for six months to evaluate abstinence. Results: Quit line enrolment rate of the intervention group was 55% (22) compared to 7.5% (3) in the control (P < 0.001 95% CI 30.1 - 64.9). In the intervention group 27% (6) sustained quitting for six months compared to none in the control group. Conclusion: This study has shown that brief advice for smoking cessation is more effective than an information leaflet alone to promote quitting and that to maintain abstinence quit line follow up is necessary. Larger samples size and longer follow up studies are needed to further confirm these findings.

Abstract The use of computer based learning models in medical domain has become a significant area of research. Organ transplantation is one of the main areas where prognosis models are being used for predicting the survival of patients. Post transplantation mortality rate is reduced if there exists an intelligent system that can pick out the correct donorrecipients pairs from a pool of donor and recipient data. In this paper, we propose a survival prediction model to define three month mortality of patients after liver transplantation. We used an Artificial Neural Network model for the survival rate of liver transplantation. The data for the study was gathered from United Network for Organ Sharing transplant registry. The main objective of the study is to develop a model for short-term survival prediction of liver patients. With 10-fold cross validation we were divided the whole data into training and test data which gives an accuracy of 99.74 % by Multilayer Perceptron Artificial Neural Network model. We also compared the model with other classification models using various error performance measures. To ensure accuracy we experimented our model with existing models and proved the result.

Abstract
Depression is a commonly diagnosed mental health illness among people of all ages. Individuals, who have children that battle with depression, or struggle with it themselves, may seek information about depression and treatment options for their diagnosis online. However, there is little research about the quality and reading level of online information for this health condition. The purpose of this study is to evaluate the quality and readability of online patient education information on the topic of depression and parents’ comprehension of this information to make sound health decisions for their children who may show signs and symptoms of depression