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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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A general framework for developing computable clinical phenotype algorithms.

David S Carrell¹, James S Floyd^2,3, Susan Gruber⁴

¹Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States.

Journal of the American Medical Informatics Association : JAMIA

|May 15, 2024

Summary

This summary is machine-generated.

This study introduces a 5-stage framework to guide developers in creating computable algorithms for patient phenotyping using electronic health record data. The framework enhances algorithm development through machine learning and natural language processing.

Keywords:

computable algorithms health outcomes modeling methods recommended practices

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Area of Science:

Biomedical Informatics
Clinical Data Science
Computational Medicine

Background:

Accurate identification of patient clinical conditions (phenotypes) is crucial for research and clinical care.
Leveraging rich electronic health record (EHR) data requires robust computable algorithms.
Existing methods for algorithm development can be improved through structured guidance.

Purpose of the Study:

To present a general framework for developing computable algorithms to identify patient phenotypes.
To provide high-level guidance for incorporating diverse EHR data using methods like machine learning (ML) and natural language processing (NLP).
To enhance the efficiency and reliability of the algorithm development process.

Main Methods:

Conceptualized a framework based on extensive prior phenotyping experiences.
Incorporated insights from three dedicated algorithm development projects.
Assembled a multidisciplinary team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and data science.

Main Results:

Proposed a 5-stage algorithm development framework: (1) assessing fitness-for-purpose, (2) creating gold standard data, (3) feature engineering, (4) model development, and (5) model evaluation.
Detailed principles, strategies, and practical guidelines for each stage.
The framework aims to improve the development of patient phenotyping algorithms.

Conclusions:

The presented framework offers practical guidance for developers of computable phenotyping algorithms.
It serves as a foundation for future research and extensions in clinical algorithm development.
This structured approach facilitates the effective use of EHR data for identifying specific patient conditions.