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PredicT-ML: a tool for automating machine learning model building with big clinical data.

Gang Luo1

  • 1Department of Biomedical Informatics, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, UT 84108 USA.

Health Information Science and Systems
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

PredicT-ML automates machine learning for big clinical data, overcoming barriers in algorithm selection and temporal data aggregation. This empowers healthcare professionals to leverage predictive modeling for improved outcomes and reduced costs.

Keywords:
Automated temporal aggregationAutomatic algorithm selectionAutomatic hyper-parameter value selectionBig clinical dataMachine learning

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Area of Science:

  • Healthcare Informatics
  • Machine Learning in Medicine
  • Clinical Data Science

Background:

  • Predictive modeling with big clinical data is crucial for healthcare applications.
  • Machine learning (ML) use in healthcare faces barriers: complex algorithm/hyper-parameter selection and temporal data aggregation challenges.
  • These barriers create bottlenecks, limiting the use of predictive models for improving healthcare outcomes and reducing costs.

Purpose of the Study:

  • To introduce PredicT-ML, a software system designed to automate machine learning model building with big clinical data.
  • To overcome the identified barriers in ML model development for healthcare applications.

Main Methods:

  • The paper details the design and vision of the PredicT-ML software system.
  • PredicT-ML aims to automate the selection of ML algorithms and hyper-parameters.
  • The system is designed to handle temporal aggregation of clinical data.

Main Results:

  • The detailed design of the PredicT-ML system is presented.
  • The system is engineered to streamline the process of building predictive models from big clinical data.

Conclusions:

  • PredicT-ML is expected to democratize the use of big clinical data for thousands of healthcare administrators and researchers.
  • The tool will enhance the ability to advance clinical research and improve healthcare delivery.