Improving MASLD Identification in Patients with T2D: A Computable Phenotype Framework Integrating Structured Data and Clinical Notes
- 1University of Florida
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent among individuals with type 2 diabetes (T2D), yet accurate identification from electronic health records (EHRs) remains challenging because clinically relevant information is fragmented across structured data and unstructured clinical documentation. We developed a rule-based computable phenotype (CP) framework that integrates structured EHRs with clinical notes for scalable MASLD identification in patients with T2D. The framework comprises five complementary CP algorithms derived from validated fibrosis indices and liver injury biomarkers, together with a lightweight natural language processing module that identifies MASLD-related evidence using keyword matching, contextual filtering, and negation detection. The proposed framework was systematically evaluated across multiple threshold settings, with additional validation through age- and sex-stratified analyses and manual chart review. The findings demonstrate that integrating structured and unstructured EHR data provides an accurate and scalable framework for real-world MASLD phenotyping and offers a practical strategy for developing CPs for other chronic diseases.
Related
- Improving Post‐Fall Assessment Documentation in Older Inpatients Using a Structured Falls Proforma: A Retrospective Cohort Study
- Nomogram models integrating multiregion radiomics, quantitative computed tomography, and clinical indicators for distinguishing connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis and improving the prediction of gender-age-physiology stage
- A latent metabolic dysfunction phenotype integrating insulin resistance and leptin is associated with disease severity in COPD
- Identifying a Cardio‐Hepatic Phenotype by Integrating the Albumin‐Bilirubin Score Into Risk Assessment in Pulmonary Arterial Hypertension
- Integrating emergency medical services and hospital surge capacity for mass-gathering preparedness: a structured narrative review
BibTeX
@misc{chen2026improving,
title = {{Improving MASLD Identification in Patients with T2D: A Computable Phenotype Framework Integrating Structured Data and Clinical Notes}},
author = {Y. Chen and A. Sharma and Q. Yang and Y. Chen and R. Yin},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.26.26364078},
url = {https://www.medrxiv.org/content/10.64898/2026.09.26.26364078v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Improving MASLD Identification in Patients with T2D: A Computable Phenotype Framework Integrating Structured Data and Clinical Notes
AU - Y. Chen
AU - A. Sharma
AU - Q. Yang
AU - Y. Chen
AU - R. Yin
PY - 2026
DA - 2026/09/26
PB - medRxiv
DO - 10.64898/2026.09.26.26364078
UR - https://www.medrxiv.org/content/10.64898/2026.09.26.26364078v1
AB - Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent among individuals with type 2 diabetes (T2D), yet accurate identification from electronic health records (EHRs) remains challenging because clinically relevant information is fragmented across structured data and unstructured clinical documentation. We developed a rule-based computable phenotype (CP) framework that integrates structured EHRs with clinical notes for scalable MASLD identification in patients with T2D. The framework comprises five complementary CP algorithms derived from validated fibrosis indices and liver injury biomarkers, together with a lightweight natural language processing module that identifies MASLD-related evidence using keyword matching, contextual filtering, and negation detection. The proposed framework was systematically evaluated across multiple threshold settings, with additional validation through age- and sex-stratified analyses and manual chart review. The findings demonstrate that integrating structured and unstructured EHR data provides an accurate and scalable framework for real-world MASLD phenotyping and offers a practical strategy for developing CPs for other chronic diseases.
ER -