Innovative Statistics and Machine Learning for Precision Medicine

Precision medicine is an emerging practice of medicine that uses a patient's specific characteristics, such as genetic information, health history, environmental exposure, and needs and preferences, to guide decisions made with regard to the prevention, diagnosis, and treatment of diseases. Stimulated by the advancements in genomics and medical imaging, exciting and remarkable progress has been made in precision medicine. The challenges of statistical and machine learning analysis of precision medicine include: heterogeneity (due to known and unknown disease subtypes), high-dimensionality (large number of predictors for prognostic or genomic information), limited number of samples, the need to integrate multiple data types, and the complexity of underlying biochemical mechanisms. These challenges raise many open problems that require urgent attention. The workshop aims to bring together statisticians, biostatisticians, data scientists, healthcare domain experts, and graduate students to exchange new ideas on the state-of-the-art and challenges in the research on statistical and machine learning methods for precision medicine.