Key techniques in the EWs algorithm Temporal bucketing Discriminative classification Bootstrap aggregating(bagging) Exploratory under-sampling Exponential moving average smoothing Kernel-density estimation
Key Techniques in the EWS Algorithm • Temporal bucketing • Discriminative classification • Bootstrap aggregating (bagging) • Exploratory under-sampling • Exponential moving average smoothing • Kernel-density estimation
Workflow of the System Data set d. t Real-time data stream Data Preprocessing Generate a 24-hour window Bucketing Data Preprocessing Exploratory Undersampling Bucket bagging Bucketing Logistic Regression Final Model EMA Smoothing Predict Model threshold? iteration cour Yes larm Warni Final Model (A)Model building phase Deployment phase
Workflow of the System Exploratory Undersampling Logistic Regression Bucket bagging Data set D,T Converge? Predict Model Final Model Yes No Real-time data stream Final Model EMA Smoothing > threshold? Alarm Warning No Data Preprocessing > iteration count? Bucketing Yes No Bucketing Data Preprocessing (A) Model building phase (B) Deployment phase Generate a 24-hour window Yes
Data Preprocessing outler 由吾<四四四母普晋日a是 Outlier removal Normalization
Data Preprocessing Outlier removal Normalization
Temporal Bucketing We retain data in a sliding window of the last 24 hours and divided it evenly into 6 buckets In order to capture temporal variations we compute several feature values for each bucket, including the minimum, maximum, and average
Temporal Bucketing We retain data in a sliding window of the last 24 hours and divided it evenly into 6 buckets In order to capture temporal variations, we compute several feature values for each bucket, including the minimum, maximum, and average Bucket 1 Bucket 2 Bucket 3 Bucket 4 Bucket 5 Bucket 6
Discriminative classification Logistic regression(LR Clinical data Support vector machine(SVM Data preprocessing Use max, min, and avg of each bucket and each vital sign as the Temporal Bucketing input features. ( 400 features in total) Classification Algo Use the training data to learn the model parameters Output Model, Threshold
Discriminative Classification Clinical data Data preprocessing Classification Algo. Output Model, Threshold • Logistic regression (LR) • Support vector machine (SVM) • Use max, min, and avg of each bucket and each vital sign as the input features. (~ 400 features in total) • Use the training data to learn the model parameters. Temporal Bucketing