This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
For the first paper that we hope to submit to the IEEE Healthcare Innovations and Point of Care Technologies conference in Bethesda, Maryland, we are going to focus solely on the first "Phase" of data. We have also decided that we want to work with the manufacturer's classifications of sleep/wake instead of the inactive minutes approach. This is because we want more time to refine the Inactive Minutes approach and because we found the source of the data to be a reliable sleep/wake classification method.
Working only with a single-phase removes one grouping factor from the experimentation we hope to do. Although I performed experiments on Phase 1 and Phase 2 data together, I now need to experiment solely with Phase 1. We will vary the prediction model (from sklearn: decision trees, random forests, SVM), k, length of sequence and period for which minutes will be predicted (Daytime or Nighttime). We also want to include more subject features in each sequence's attributes, such as the Rehabilitation Efficiency Ratio (RER) which is calculated by the change in overall FIM score divided by the subject's length of stay in the hospital. This is a metric that conveys the rate at which the patient's cognitive and motor abilities changed during rehabilitation.
Going forward, we hope to look more into the following:
1. Seeing if there is any relationship between subject age and other features
2. Seeing if we can work with gender subgroupings
Working only with a single-phase removes one grouping factor from the experimentation we hope to do. Although I performed experiments on Phase 1 and Phase 2 data together, I now need to experiment solely with Phase 1. We will vary the prediction model (from sklearn: decision trees, random forests, SVM), k, length of sequence and period for which minutes will be predicted (Daytime or Nighttime). We also want to include more subject features in each sequence's attributes, such as the Rehabilitation Efficiency Ratio (RER) which is calculated by the change in overall FIM score divided by the subject's length of stay in the hospital. This is a metric that conveys the rate at which the patient's cognitive and motor abilities changed during rehabilitation.
Going forward, we hope to look more into the following:
1. Seeing if there is any relationship between subject age and other features
2. Seeing if we can work with gender subgroupings
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