title: MLBCD: a machine learning tool for big clinical data.
abstract: BACKGROUND
  Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise.
  
  
  METHODS
  This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data.
  
  
  RESULTS
  The paper describes MLBCD's design in detail.
  
  
  CONCLUSIONS
  By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT 84108 USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Luo
keywords: Automatic algorithm selection; Automatic hyper-parameter value selection; Big clinical data; Entity–Attribute–Value; Machine learning; Pivot