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Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions.

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Ontologies enhance machine learning for analyzing clinical data. This review highlights key 2018-2019 papers on using ontologies with machine learning for phenotype identification and natural language processing in electronic health records.

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Knowledge Representation

Background:

  • Vast amounts of clinical data are available for research.
  • Machine learning (ML) is increasingly used for predictive analytics, precision medicine, and diagnosis.
  • Ontologies are crucial for extracting, coding, and analyzing clinical information for ML.

Purpose of the Study:

  • To review and summarize relevant 2018-2019 literature on Ontologies and Knowledge Representation.
  • To focus on the intersection of ontologies and machine learning in medical informatics.
  • To identify key applications and methodologies in this rapidly evolving field.

Main Methods:

  • Comprehensive review of medical informatics literature from 2018-2019.
  • Selection of papers demonstrating the utility of ontologies for computational analysis, including ML.
  • Categorization of selected articles into major themes.

Main Results:

  • Fifteen articles were selected, focusing on three themes.
  • Theme 1: Identifying phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology.
  • Theme 2: Employing word and node embedding algorithms for natural language processing (NLP) of EHRs and medical texts.
  • Theme 3: Developing hybrid ontology and NLP approaches for extracting structured and unstructured EHR data.

Conclusions:

  • The synergy between machine learning and semantics represents an innovative area in clinical research.
  • Ontologies are essential for ML algorithms processing clinical data for various applications.
  • The reviewed literature demonstrates the significant impact of ontologies on advancing ML applications in healthcare.