MLBCD: a machine learning tool for big clinical data

  MLBCD: a machine learning tool for big clinical data 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…

Link to Full Article: MLBCD: a machine learning tool for big clinical data

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!