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In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered...
We review our studies on how to identify the most appropriate models of diseases, and how to determine their parameters in a quantitative manner given a short time series of biomarkers, using intermittent androgen deprivation therapy of prostate cancer as an example. Recently, it has become possible to estimate the specific parameters of individual patients within a reasonable time by employing the...
We consider the prediction of glaucomatous visual field loss based on patient datasets. It is critically important to predict how rapidly the disease is progressing in an individual patient. However, the number of measurements for each patient is so small that a reliable predictor cannot be constructed from the data of a single patient alone. In this paper, we propose a novel multi-task learning approach...
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