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A semiparametric method for addressing underdiagnosis using electronic health record data.
Weidong Ma1, Jordana B Cohen1,2, Jinbo Chen1
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
Biometrics
|December 1, 2025
Summary
Identifying underdiagnosed medical conditions is crucial for effective treatment. This study introduces a novel statistical method using electronic health records (EHRs) to accurately estimate patient risk for conditions like non-alcoholic steatohepatitis.
Area of Science:
- Medical Informatics
- Biostatistics
- Data Science
Background:
- Accurate diagnosis is essential for effective medical treatment, yet many conditions are underdiagnosed, leading to delays.
- Electronic Health Records (EHRs) contain rich patient data that can potentially identify underdiagnosed individuals.
- Existing methods struggle with the positive-unlabeled data structure inherent in EHRs, lacking data from condition-free patients.
Purpose of the Study:
- To develop a novel statistical method for identifying underdiagnosed patients using EHR data.
- To overcome the challenge of positive-unlabeled data by supplementing with ascertained condition statuses.
- To build accurate risk assessment models for medical conditions from EHR data.
Main Methods:
- Proposed a novel statistical method to handle positive-unlabeled EHR data.
- Supplemented unlabeled EHR data by ascertaining condition statuses for a subset of patients.
- Studied asymptotic properties and assessed finite-sample performance via simulation.
Main Results:
- Developed a method to estimate the probability of a patient having a specific condition.
- Demonstrated the method's effectiveness through simulation studies.
- Applied the method to identify potentially underdiagnosed non-alcoholic steatohepatitis (NASH) patients using EHR data.
Conclusions:
- The novel statistical method effectively utilizes supplemented EHR data to identify underdiagnosed patients.
- This approach can significantly improve the early detection of conditions like NASH.
- Leveraging EHR data with advanced statistical methods offers a promising avenue for addressing underdiagnosis in healthcare.
Keywords:
electronic health recordsimportance weightingpositive-unlabeledrisk predictionunderdiagnosisMore Related Videos
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