Analyses and Concerns in Precision Medicine: A Statistical Perspective
Xiaofei Chen
This article explores the critical role of statistical analysis in precision
medicine. It discusses how personalized healthcare is enhanced by statistical
methods that interpret complex, multidimensional datasets, focusing on
predictive modeling, machine learning algorithms, and data visualization
techniques. The paper addresses challenges in data integration and
interpretation, particularly with diverse data sources like electronic health
records (EHRs) and genomic data. It also delves into ethical considerations
such as patient privacy and data security. In addition, the paper highlights
the evolution of statistical analysis in medicine, core statistical
methodologies in precision medicine, and future directions in the field,
emphasizing the integration of artificial intelligence (AI) and machine
learning (ML).