@article{Forecasting:7592,
      recid = {7592},
      author = {Shi, Chengjian},
      title = {Continuous Temporal Signals and Electronic Health Records  for Broad Health States Forecasting},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2023-08},
      pages = {122},
      abstract = {The modern medical data deluge accelerated when the vast  amount of medical information gathered and stored by  electronic sensors became widely available. Medical data  are complex, heterogeneous, and continue to rapidly  accumulate in electronic databases, therefore, data-driven  statistical learning techniques have the potential to  drastically improve clinical care by anticipating clinical  complications and suggesting interventions.This  dissertation investigates the application of an assortment  of statistical learning techniques to extract instructive  patterns from raw medical data. Chapter 1 provides a brief  overview of current statistical learning methods. We also  examine both the limitations and the opportunities for  state-of-the-art developments in medical forecasting.  Chapter 2 introduces a project that began as a mere  conjecture formulated by an endocrinologist but developed  into a large-data analysis of linked pathogenesis, linking  pancreatitis and type 2 diabetes mellitus. Chapter 3  describes a study in which we collaborated with a  gerontologist interested in predicting cognitive decline in  senior patients. In this study, we attempt such predictions  by using accelerometry data collected from Chicago's south  side community and implementing advanced machine learning  methods for predicting patients' future clinical  trajectories. In Chapter 4, we identify the novel, hip  fracture risk factors and investigate whether statistical  survival analysis could improve upon existing tools'  accuracy. In Chapter 5, we constructed a state-of-art  machine learning tool on fracture detection on patients’  broad prior disease history. Lastly, Chapter 6 summarizes  the above projects and suggests future directions for our  exploration of statistical learning from complex medical  data. We also discuss our studies' potential importance for  statistical learning from medical data and outline the  problems that remain open in the field.},
      url = {http://knowledge.uchicago.edu/record/7592},
      doi = {https://doi.org/10.6082/uchicago.7592},
}