Develop and Validate a Computable Phenotype for the Identification of Alzheimer’s Disease Patients Using Electronic Health Record Data
ABSTRACT INTRODUCTION Alzheimer’s Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.METHODS We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN).RESULTS Our best-performing CP is “patient has at least 2 AD diagnoses and AD-related keywords” with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively.DISCUSSION We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 08. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
He, Xing [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2024.02.06.24302389 |
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| PPN (Katalog-ID): |
XBI042419565 |
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| 520 | |a ABSTRACT INTRODUCTION Alzheimer’s Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.METHODS We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN).RESULTS Our best-performing CP is “patient has at least 2 AD diagnoses and AD-related keywords” with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively.DISCUSSION We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs. | ||
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| 700 | 1 | |a Lyu, Tianchen |4 aut | |
| 700 | 1 | |a Bost, Sarah |4 aut | |
| 700 | 1 | |a Tong, Jiayi |4 aut | |
| 700 | 1 | |a Li, Lu |4 aut | |
| 700 | 1 | |a Zhou, Yujia |4 aut | |
| 700 | 1 | |a Guo, Jingchuan |4 aut | |
| 700 | 1 | |a Tang, Huilin |4 aut | |
| 700 | 1 | |a Wang, Fei |4 aut | |
| 700 | 1 | |a DeKosky, Steven |4 aut | |
| 700 | 1 | |a Xu, Hua |4 aut | |
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| 700 | 1 | |a Xu, Jie |4 aut | |
| 700 | 1 | |a Guo, Yi |4 aut | |
| 700 | 1 | |a Wu, Yonghui |4 aut | |
| 700 | 1 | |a Bian, Jiang |0 (orcid)0000-0002-2238-5429 |4 aut | |
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