A deep state-space analysis framework for cancer patient latent state estimation and classification from EHR time-series data
Fig 3
(a) A bubble plot depicting the percentage of outliers for the top 10 items with a large difference in distribution (Wasserstein distance) between clusters.
The size of the bubble indicates the percentage of abnormally low, normal, and abnormally high values in each cluster. The x-axis indicates whether test values are abnormally low, normal, or abnormally high; the y-axis corresponds to the type of test item, and the color corresponds to the cluster. The items are arranged in descending order based on the magnitude of the difference in distribution between clusters. (b) A stacked bar graph illustrates the percentage of abnormal values for the top 2 items, HGB and HCT, with a difference in distribution between clusters. The graph shows the percentage of abnormal values for HGB and HCT in each drug-administered cancer patient and each cluster. In all patients with cancer receiving chemotherapy, there is a higher percentage of abnormally low values for HGB and HCT in Cluster I, which is a dangerous state. (c) For each patient with cancer receiving drug treatment, we examined the characteristic differences in the distribution between clusters for lymphocytes and segmented neutrophils values. The graph illustrates the percentage of abnormal values for each drug-administered cancer patient in each cluster. In patients treated with Afatinib, Nivolumab, and Osimertinib, there is a higher proportion of abnormally low values for lymphocytes and abnormally high values for segmented neutrophils in Cluster I, which is a dangerous state.