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Data Validation

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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Related Experiment Video

Updated: Jan 13, 2026

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and

Hao Dai¹, Elliot B Tapper², Lili Zhao³

¹Department of Biostatistics & Health Data Science, Indiana University School of Medicine, 410 W 10th St, Indianapolis, IN 46202, United States.

Alcohol and Alcoholism (Oxford, Oxfordshire)

|January 8, 2026

Summary

This summary is machine-generated.

Accurate identification of Alcohol Use Disorder (AUD) in electronic health records is improved using computable phenotypes (CPs). These CPs integrate diverse data, outperforming traditional ICD codes for research and patient care.

Keywords:

alcohol use disorder computable phenotype natural language processing real-world evidence

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Last Updated: Jan 13, 2026

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Published on: May 15, 2020

Area of Science:

Biomedical Informatics
Clinical Research Informatics

Background:

Alcohol Use Disorder (AUD) contributes to significant morbidity, particularly liver disease.
Accurate AUD identification in electronic health records (EHRs) is crucial for research and clinical care.
Existing International Classification of Diseases (ICD) code-based methods have limitations in case identification, and manual review is not scalable.

Purpose of the Study:

To develop and validate computable phenotypes (CPs) for accurate AUD identification using integrated EHR data.
To improve upon the limitations of traditional ICD code-based algorithms for AUD case ascertainment.
To create a scalable solution for identifying patients with AUD for research, surveillance, and quality improvement.

Main Methods:

Developed AUD CPs using a two-step process on a large EHR dataset (2 million patients).
Candidate cohorts were identified using AUD-related ICD codes, medications, and keyword searches (structured and unstructured data).
Rule-based algorithms were iteratively refined via manual chart review and evaluated for sensitivity, positive predictive value (PPV), and F1-score, with validation on independent datasets.

Main Results:

The F1-optimized CP achieved an F1-score of 0.87 (sensitivity: 0.98, PPV: 0.78).
The precision-optimized CP achieved a PPV of 0.9 (sensitivity: 0.68, F1-score: 0.77).
CPs demonstrated robustness and generalizability with minimal performance differences between training and testing sets, significantly outperforming ICD-only approaches.

Conclusions:

Computable phenotypes (CPs) integrating structured and unstructured EHR data provide accurate and reproducible AUD identification.
This approach surpasses traditional AUD-specific ICD-based methods in performance.
CPs facilitate efficient cohort construction for clinical research, public health surveillance, and quality improvement initiatives for AUD.