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Methods of Documentation VII: EMR

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Classifying Refugee Status Using Common Features in EMR.

Malia Morrison¹, Vanessa Nobles¹, Crista E Johnson-Agbakwu^2,3,4,5

¹College of Health Solutions, Arizona State University, Phoenix, AZ, 85004, USA.

Chemistry & Biodiversity

|September 2, 2022

Summary

This summary is machine-generated.

A new machine-learning method, the refugee identification system (RIS), accurately identifies refugees in healthcare data. This tool aids in understanding refugee health needs and reducing healthcare disparities.

Keywords:

health disparity health informatics machine learning refugee health

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Health Informatics
Machine Learning
Public Health

Background:

Accurate identification of refugees in healthcare databases is crucial for addressing their specific health needs.
Vulnerable refugee populations often face significant health disparities.
Existing methods for refugee identification in health data are limited.

Purpose of the Study:

To develop and validate a machine-learning model for automated and accurate refugee identification in healthcare databases.
To facilitate large-scale investigations into refugee healthcare requirements.
To contribute to the reduction of health disparities among refugees.

Main Methods:

A dataset of 103 refugees and 930 non-refugees in Arizona was curated.
Individual-level data (age, language, masked address) and statistical data were compiled.
Feature engineering was applied to quantitative data, followed by random forest model development for classification.

Main Results:

The refugee identification system (RIS) demonstrated high classification accuracy (overall accuracy=0.97).
Key performance metrics included high specificity (0.99) and a strong area under the ROC curve (0.98).
The model achieved a sensitivity of 0.85 and a positive predictive value of 0.88.

Conclusions:

The developed refugee identification system (RIS) provides an accurate and automated method for identifying refugees in health databases.
RIS is adaptable for use beyond Arizona, enabling broader application.
This tool can significantly enhance the study of refugee health and inform interventions to improve health equity.