## 4 Experiments
### Datasets  
We performed our comparison and ablation experiments on two datasets. The first dataset is the UCI Human Activity Recognition (HAR) dataset [52]. It is a widely used benchmark dataset in the field of human activity recognition. This dataset includes information from 30 volunteers (ages 19 to 48) who used their smartphones to record sensor data while doing six everyday activities. The activities were WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, and LAYING. UCI HAR contains a total of 10,299 samples, which we divide into a training set (7352 samples, about 71%) and a test set (2947 samples, about 29%). We also divide 20% of the data in the training set as a validation set for optimizing the hyper-parameters and implementing the early stopping strategy. Each data sample has nine signal channels. These channels include total acceleration (X, Y, and Z axes), body acceleration (X, Y, and Z axes), and body gyroscope signals (X, Y, and Z axes). Initially, the signals were processed by a filter that reduced noise. Then, they were divided into segments using a fixed-width sliding window (2.56 s, 128 samples) with 50% window overlap. To prepare the data, we use a sliding window strategy. This means that we divide the sensor timing data into sections of 128 points each. Each section is 2.56 s of data, and the sections are 50% the same as the sections in the window right before it. We apply the MinMaxScaler technique to normalize all the feature values to the interval \([0,1]\). This technique eliminates the scale difference between different features. It does so to ensure the stability of model training.  
The second dataset was based on multivariate time-series data from the MIMIC-IV clinical database [53]. This database documents the hospitalization of patients in the intensive care unit (ICU). The dataset is organized by patients and includes their important health information and details about the treatment they received during their stay in the ICU. The data were created using a sliding window approach. This means that a time_step was generated at 1 h intervals. These time steps started from the patient's time of admission to the ICU. Each time step is the same as a 3 h observation window. Each window shows the patient's status during that time period. Each time step has the patient's main physiological information, like their average heart rate and average systolic blood pressure. It also has their lab results, like their highest platelet count and highest D-dimer value. And it has infoon any medical treatments they received, like if they received anticoagulant therapy, when they were in the hospital, and if they were diagnosed with a blood clot-related disease.The dataset that was obtained after using SQL to make a request contained 130,000 samples. All of the samples were separated into three sets: a training set, a test set, and a validation set. We used a tool called StandardScaler in the scikit-learn library to adjust each feature so that the mean was 0 and the standard deviation was 1. This made the features more comparable and prepared the data for training the model. For the thrombus prediction task, the MIMIC-IV dataset was divided into a training set (64%), a validation set (16%), and a held-out test set (20%), with all splits being stratified.