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Anteromesial Temporal Lobectomy for Medically Intractable Temporal Lobe Epilepsy: An Operative Study
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Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization.

Ioakeim Perros1, Evangelos E Papalexakis2, Richard Vuduc1

  • 1Georgia Institute of Technology, United States.

Journal of Biomedical Informatics
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

This study used PARAFAC2 on electronic health records to identify four key phenotypes in medically complex children. This computational approach aids in understanding patient conditions and improving clinical outcomes.

Keywords:
Computational phenotypingTemporal phenotypingTensor analysis

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Area of Science:

  • Computational biology
  • Health informatics
  • Pediatric medicine

Background:

  • Medically complex children consume significant healthcare resources with suboptimal outcomes.
  • Electronic health records (EHRs) offer rich data for understanding complex conditions.
  • Manual chart review is time-consuming for phenotype extraction.

Purpose of the Study:

  • To computationally extract clinically meaningful phenotypes from longitudinal EHRs of medically complex children.
  • To identify phenotypes and their temporal evolution in a scalable manner.
  • To avoid manual chart review for phenotype identification.

Main Methods:

  • Analysis of longitudinal EHRs from 1045 medically complex patients.
  • Application of the PARAFAC2 tensor factorization method.
  • Extraction of patient representations and temporal phenotype signatures.

Main Results:

  • Identification of four distinct phenotypes: gastrointestinal, oncological, blood-related, and neurological disorders.
  • Demonstration of patient representations for survival variation analysis.
  • Showcasing of temporal phenotypic trends for individual patients.

Conclusions:

  • PARAFAC2 enables unsupervised temporal phenotyping with variable patient record lengths.
  • The method minimizes the burden on clinical experts for phenotype validation.
  • Computational phenotypes have applications in decision support, mortality prediction, and clinical trial recruitment.