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Feature extraction for phenotyping from semantic and knowledge resources.

Wenxin Ning1, Stephanie Chan2, Andrew Beam3

  • 1Department of Industrial Engineering, Tsinghua University, Beijing, China.

Journal of Biomedical Informatics
|February 11, 2019
PubMed
Summary
This summary is machine-generated.

A new method called SEmantics-Driven Feature Extraction (SEDFE) automates the selection of features for phenotyping algorithms using medical knowledge, improving efficiency and accuracy in electronic health record analysis.

Keywords:
Distributional semanticsElectronic health recordsMachine learningPhenotyping

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Health Data Science

Background:

  • Phenotyping algorithms identify patient cohorts from electronic health records (EHRs) for research.
  • Existing unsupervised methods require manual parameter tuning for each phenotype.
  • Accelerating EHR phenotyping algorithm development is crucial for clinical and genomic studies.

Purpose of the Study:

  • To propose a fully automated, robust, and EHR-independent unsupervised feature selection method.
  • To leverage publicly available medical knowledge sources for phenotyping algorithm development.
  • To enhance the efficiency and accuracy of high-throughput phenotyping.

Main Methods:

  • SEmantics-Driven Feature Extraction (SEDFE) utilizes online medical knowledge sources for candidate features.
  • Features are represented using distributional semantics via neural word embeddings and UMLS Metathesaurus.
  • A linear decomposition criterion selects semantically relevant features for classification.

Main Results:

  • SEDFE achieved comparable accuracy to the EHR-based SAFE algorithm and expert-curated features.
  • Performance was evaluated on five distinct phenotypes, including coronary artery disease and rheumatoid arthritis.
  • The SEDFE method demonstrated robustness to variations in input semantic vectors.

Conclusions:

  • SEDFE offers a fully automated and EHR-independent approach to unsupervised feature selection for phenotyping.
  • The method achieves satisfying performance, promising greater efficiency and accuracy in algorithm development.
  • SEDFE facilitates high-throughput phenotyping by reducing reliance on manual intervention and EHR data.