Background & Purpose

This course covers some of the statistical foundations for medical AI. In particular, the topics include linear regression, logistic regression, ROC curve analysis, Poisson regression, and survival analysis. The course will start from continuous data to illustrate the rationale of the linear regression model and its applications. The concept will be extended to different data structures, including binary data, count data, and survival data. Another topic of this course will focus on the implementation of the R software and the ability to analyze the real data sets. We expect students can choose the appropriate methods (as well as understand the rationale of the methods) to conduct the statistical analysis for a given data set correctly.

本課程涵蓋可用於醫學AI的統計分析基礎。主要主題包括線性迴歸，邏輯迴歸，ROC曲線分析，泊松迴歸和生存分析。本課程將從連續型資料開始，介紹迴歸分析的理論架構以及其應用方式，然後再推廣到不同資料結構的分析方法，包含二元資料、計數資料、以及存活資料。本課程的另一個重心放在R軟體的操作以及實際資料的分析。希望修課的學生可以根據資料的結構，選擇適當的分析方法，並了解其運作原理，完成正確的統計分析。