Label efficient phenotyping for Long COVID using electronic health records.

Publication date: Jul 04, 2025

Long COVID poses a significant disease burden globally, but its heterogeneous presentation and unreliable coding practices render it difficult to study. Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. We introduce the LAbel-efficienT Long COVID pHenotyping (LATCH) algorithm, which synthesizes a small number of gold-standard labels and a large, unlabeled dataset with many electronic health record (EHR) features. Both internal validation and external validation demonstrated the superior performance of LATCH over methods using the U09. 9 Long COVID EHR code alone. Our downstream analysis revealed a pattern of elevated healthcare utilization due to Long COVID, peaking at and continuing beyond the fourth month following COVID infection. LATCH enhances the classification of Long COVID by fully utilizing both labeled and unlabeled data, providing vital insights into healthcare utilization trends, informing clinical and public health responses to the enduring consequences of COVID-19.

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Concepts Keywords
Algorithms Covid
Gold Efficient
Healthcare Ehr
Month Electronic
Unreliable Healthcare
Label
Latch
Long
Phenotyping
Poses
Records
Significant
Unlabeled
Utilization
Validation

Semantics

Type Source Name
disease MESH Long COVID
disease IDO algorithm
drug DRUGBANK Gold
disease MESH infection
disease MESH COVID-19
drug DRUGBANK Carboxyamidotriazole
disease IDO acute infection
disease MESH Inflammation
drug DRUGBANK Tretamine
disease IDO site
disease IDO entity
disease IDO symptom
drug DRUGBANK Aspartame
disease IDO process
disease MESH uncertainty
drug DRUGBANK L-Phenylalanine
disease MESH clinical relevance
drug DRUGBANK Coenzyme M
disease MESH privacy
disease MESH cognitive impairment
disease MESH Syndrome
disease MESH sequelae
disease MESH thromboinflammation
pathway REACTOME Reproduction

Original Article

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