* If we can predict homeless people’s behaviour, we will able to provide help and services with these people. * Among all methods, machine learning techniques have been proved that they are able to improve the decision making in the health-care sector (Chen et al., 2019). * Session-based recommenders are useful when we have user interaction history that they can learn based on the short-term interaction (Wang et al., 2022). These methods are emerging in the healthcare system to recommend the next-treatment recommendation (Haas, n.d.). * Our Aim is to predict the event within a session. * We used Word2vec model (Rong, 2016) that that capture the semantic similarities to predict the next event.
* In this work we used the MLB public dataset to represent the medical data. * The features in this dataset are correlated with the features that we will see in the real dataset. That’s why this dataset represents health care dataset. * The data contains, a series of discreet events, including medical tests that can come back with good or bad results or vital crash that needs emergency or intense medical aid. * Another type of events in our database are stretched over a period. These events have starting and ending point
By using job arrays and creating a loop in the shell * Submitted several jobs on the GPU partition. * Each job had unique input to do hyper parameter optimization * We have successfully received the results for about 200 jobs
* Submitted several jobs on the GPU partition. * In each job I trained the model with 1500 epoch * Each job took about 40 mins on GPU (about 6 hours on CPU partition) * By observing the jobs on the cluster each time 12 jobs was running in parallel. * Whole of the experiments took about 5 days in the cluster which is almost equal to the 60 days in personal laptop. * Could use the cluster to find the best parameters almost 10 times faster than using my own resource. * Found clusters recourses very useful and and they are time saving for doing experiments