Biomedical subjects

So Young Sohn

Publications and source records attributed to So Young Sohn.

2 recordsLinked to original sources

PubMed2003

A study on the student pilot's mental workload due to personality types of both instructor and student.

The main purpose of this paper is to provide the ideal flight crew combination for instructor and student pilots in order to enhance the flight training effects based on Myers Briggs Type Indicator (MBTI) theory. In addition to personality, various levels of flight difficulty are considered in order to investigate their potential interaction effect in terms of student pilots' mental workload measured in heart rate, altitude deviation, NASA-TLX (Task Load Index) and subjective degree of personality harmony. Based on an experiment performed in a real flight situation, we found significant effects of personality combinations in terms of all four outcome measures. Both group C types of instructors and students who are concrete, realistic and have mechanical skills turn out to be the ideal flight crew combination. A structural equation model, fitted to analyse causality among the four response variables, implied that as the heart rate increased, the altitude deviation increased. In addition, as the altitude deviation and personality harmony increased, NASA-TLX increased. The results of this study are expected to provide a theoretical basis for manning the flight crew combinations and thereby enhancing the efficiency of flight training.

Aviation↗

PubMed2003

Screening test data analysis for liver disease prediction model using growth curve.

This study was done based on screening test data accumulated from 1994 to 2001 for studying of risk factor related with liver disease and prediction model of liver disease. In the existing study related with liver, the main current is studying on liver cancer, not on liver disease, previous step into liver cancer. As a result of estimating prediction model through the risk factors of liver disease and the growth curve on the basis of data, it is shown that most of the risk factors about liver disease are also those about known well as liver cancer. In addition, to investigate liver disease prevalence from the viewpoint of the future, this study presumed risk factor through the various growth curve analysis and examined logistic regression, decision tree and neural network from those estimators. In the case of neural network using growth curve estimator of Xi(5)=alphai+betaiT+epsiloniT, accuracy of liver disease was 72.55% and sensitivity was 78.62%. On the other hand, in the case of liver disease prediction model using recent screening test data estimator, accuracy was 72.09% and sensitivity was 71.72%. Those are lower than liver disease prediction model of growth curve analysis. In the various liver disease prediction models assumed by growth curve and many distinction models, when growth curve estimator was used, sensitivity value was improved.

Humans↗