METHOD: Seven volunteer post-call doctors were recruited to go through an EEG recording before and after their on-call rotation while at rest and subsequently while carrying out Stroop Test, putting their cognitive function at work.
RESULTS: The doctors have worked up to 33 hours in a row and have had sleep of an average of 1.5 hours. It is found that during task there is a statistically significant increase in theta (frontal and occipital regions) and beta (occipital region) band power while at task post-call. Alpha band power is increased in the frontal and reduced in other regions. Correlation with Stroop Test results indicated that those who have higher alpha, beta, and lower relative theta powers at the frontal region at post-call rest have higher percentage of correct congruent trials.
CONCLUSION: The results objectively imply that these fatigue doctors are under more strain while carrying out a task and corresponds to the implicated regions of brain stimulated by the task accordingly.
SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.
METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.
CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.