Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

Journal: Journal of biomedical semantics
Published Date: Sep 16, 2017

Abstract

BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data.

Authors

  • Hua Min
    Hua Min, Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA 22030-4444, USA, E-mail: hmin3@gmu.edu.
  • Hedyeh Mobahi
    Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA.
  • Katherine Irvin
    Department of Health Administration and Policy, College of Health and Human Services, George Mason University, MS: 1J3, 4400 University Drive, Fairfax, VA, 22030-4444, USA.
  • Sanja Avramovic
    Deprtment of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, Virginia, United States.
  • Janusz Wojtusiak
    Deprtment of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, Virginia, United States.

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