Research on the Application and Interpretability of Predictive Statistical Data Analysis Methods in Medicine.

The article discusses the application and interpretability of predictive statistical data analysis methods in medicine. It highlights the importance of understanding medical phenomena and intervening to change outcomes, rather than solely focusing on prediction. The article acknowledges the potential of artificial intelligence (AI) methods in medicine, but also raises concerns about the lack of transparency and interpretability in predictive algorithms. It explores various methods, such as explainable AI (XAI) and visualizations, that aim to address these challenges. The article also emphasizes the need for combining different analysis methods, such as regression models and machine learning, to obtain reliable and interpretable results. Additionally, it discusses the role of meta-analysis in evidence-based medicine and the challenges of synthesizing results from predictive models. Overall, the article provides insights into the complexity and potential of predictive statistical data analysis methods in medicine. [Extracted from the article]