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Article

Predicting nonsurgical treatment outcomes in lumbar disc herniation: leveraging sparse electronic health records for patient phenotyping.

International journal of medical informatics - 1 Dec 2025

Lee Ye-Seul, Lee Yoon Jae, Ha In-Hyuk

Abstract excerpt

OBJECTIVE: Electronic health records (EHRs) offer a wealth of patient data but often fail to capture the dynamic progression of symptoms, particularly in non-continuously monitored conditions like lumbar disc herniation (LDH). This study aimed to enhance the prediction of nonsurgical treatment outcomes for LDH using latent class trajectory modeling (LCTM) combined with machine learning, leveraging sparse EHRs to...

Read the complete abstract on PubMed

Topics

Humans
Electronic Health Records
Intervertebral Disc Displacement
Male
Female
Machine Learning

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Middle Aged

Treatment Outcome

Phenotype

Lumbar Vertebrae

Adult

Source

Identifiers

PMID: 40729776
DOI: 10.1016/j.ijmedinf.2025.106056

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