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Article

Evaluating unsupervised and rule-based phenotyping methods versus administrative code counts for systemic sclerosis identification.

Seminars in arthritis and rheumatism - 1 Oct 2026

Luo Yiming, Zhang Gongbo, Agarwal Aradhna, Weng Chunhua, Bernstein Elana J

Abstract excerpt

OBJECTIVE: Our study aims to evaluate the performance of an unsupervised machine learning approach, linear combinations of principal components (LPC); a rule-based approach, phenotype risk score (PheRS); and M34 International Classification of Disease (ICD) code counts for identifying systemic sclerosis (SSc) and predicting mortality from electronic health record (EHR). METHODS: We conducted a single-center...

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Topics

Humans
Scleroderma, Systemic
Female
Phenotype
Middle Aged
Male
Retrospective Studies

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

Electronic Health Records

Unsupervised Machine Learning

International Classification of Diseases

Aged

Principal Component Analysis

Source

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Identifiers

PMID: 42385561
DOI: 10.1016/j.semarthrit.2026.153033
PMCID: PMC13336295

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 26 Oct 2026.

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