The purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection. Boosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.
Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.
This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice.