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lab: an R package for generating analysis-ready data from laboratory records.

Yi-Ju Tseng1,2, Chun Ju Chen3, Chia Wei Chang1

  • 1Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

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|September 14, 2023
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Summary
This summary is machine-generated.

This study introduces the R package "lab" to process electronic health record laboratory data, enabling better disease prediction. The package effectively analyzes temporal lab results, improving patient outcome predictions.

Keywords:
Analysis-ready dataExploratory data analysisLaboratory recordsR package

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Area of Science:

  • Clinical Informatics
  • Biostatistics
  • Health Data Science

Background:

  • Electronic health records (EHRs) are vital for clinical decision-making, offering insights into disease progression and treatment.
  • Laboratory test results within EHRs are crucial for predicting disease progression but present challenges due to varied units and formats.
  • Temporal data in EHRs can enhance prognoses and diagnosis, yet irregular data frequency complicates time-series analysis.

Purpose of the Study:

  • To develop an open-source R package,
  • lab
  • , to streamline the extraction and analysis of temporal information from laboratory records in EHRs.

Main Methods:

  • The
  • lab
  • package segments data into time-series windows, imputes missing values, and maps local codes to Logical Observation Identifier Names and Codes (LOINC).
  • LOINC mapping facilitates the inclusion of reference ranges, enabling normal/abnormal categorization of results.
  • Generated time-series data can be summarized statistically and used for machine learning model development.

Main Results:

  • Analysis of MIMIC-III data for newborns with patent ductus arteriosus (PDA) using the
  • lab
  • package identified significant variations in lab results associated with 30-day mortality.
  • A long short-term memory model trained on time-series data achieved an AUC of 0.83 for predicting 30-day in-hospital mortality.
  • These results highlight the package's utility in analyzing disease progression.

Conclusions:

  • The
  • lab
  • package simplifies and accelerates the extraction workflow for laboratory records.
  • This tool assists clinical data analysts in overcoming challenges posed by heterogeneous and sparse laboratory data.