Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: WFRF:(Guan Hongshu) > (2023) > Assessing clusters ...

1 av 1
Föregående post
Nästa post
Till träfflistan

Solomon, Daniel H.Brigham and Women's Hospital,Harvard Medical School (författare)

Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach

Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

2023
electronicrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:research.chalmers.se:3fbc7985-43cf-4852-bb97-60a6fba84131
https://doi.org/10.1186/s13075-023-03191-8DOI
https://research.chalmers.se/publication/538493URI

Kompletterande språkuppgifter

Språk:engelska
Sammanfattning på:engelska

Ingår i deldatabas

SwePubSwePub

Klassifikation

Ämneskategori:art swepub-publicationtype
Ämneskategori:ref swepub-contenttype

Anmärkningar

Background: Comorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes. Methods: A large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R 2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately. Results: 11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI. Conclusions: Clustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

Guan, HongshuBrigham and Women's Hospital (författare)
Johansson, Fredrik,1988Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)frejohk (författare)
Santacroce, LeahBrigham and Women's Hospital (författare)
Malley, Wendi (författare)
Guo, Lin (författare)
Litman, Heather J. (författare)
Brigham and Women's HospitalHarvard Medical School (creator_code:org_t)

Sammanhörande titlar

Ingår i:Arthritis Research and Therapy25:11478-63621478-6354

Internetlänk

Hitta via bibliotek

Arthritis Research and Therapy (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

1 av 1
Föregående post
Nästa post
Till träfflistan

Hitta mer i SwePub

Av författaren/redakt...: Solomon, Daniel ...; Guan, Hongshu; Johansson, Fredr ...; Santacroce, Leah; Malley, Wendi; Guo, Lin; visa fler...; Litman, Heather ...; visa färre...

Om ämnet

MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Reumatologi och ...

Artiklar i publikationen: Arthritis Resear ...

Av lärosätet: Chalmers tekniska högskola

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se