Accession Number:

ADA592528

Title:

Computational Gene Mapping to Analyze Continuous Automated Real-Time Vital Signs Monitoring Data

Descriptive Note:

Final rept. Jun 2012-Jun 2013

Corporate Author:

SCHOOL OF AEROSPACE MEDICINE WRIGHT PATTERSON AFB OH

Report Date:

2013-09-23

Pagination or Media Count:

21.0

Abstract:

This project explored machine learning gene mapping algorithms MLA as possible analytic platforms for advanced, forward-deployable patient care instrumentation. A patient database was developed to 1 identify physiologic data collected electronically by continuous automated critical care patient monitoring, 2 derive clinically useful vital signs VS features of potential use in predicting patient functional outcome after severe brain trauma, 3 define outcomes, 4 train and test machine learning algorithms, and 5 cross-validate and finalize results. The computer-based experimental work developed in three stages 1 feature selection by conventional univariate methodology, 2 feature selection using logistic regression, and 3 feature selection using other procedures that could modulate the limitations of the first two approaches. Stage 1 results suggested that long-term patient outcomes may be predictable using MLA and VS features gathered within the first 12 hours of critical care. Stage 2 results were much stronger but showed signs of overfitting, an inherent pitfall of feature selection using logistic regression. Stage 3 results confirmed Stage 1 and 2 results with much better correlations, particularly for early 6 weeks post discharge and late 3-6 months patient functional outcomes after severe brain trauma and using only the first 12 hours of continuous monitoring data.

Subject Categories:

  • Genetic Engineering and Molecular Biology
  • Anatomy and Physiology
  • Medicine and Medical Research
  • Numerical Mathematics
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE