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Sökning: onr:"swepub:oai:lup.lub.lu.se:d34ebe7b-6e4d-4ce4-9bbe-d6465875f68a" > Improved modeling o...

  • Daemen, Anneleen (författare)

Improved modeling of clinical data with kernel methods

  • Artikel/kapitelEngelska2012

Förlag, utgivningsår, omfång ...

  • Elsevier BV,2012
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:d34ebe7b-6e4d-4ce4-9bbe-d6465875f68a
  • https://lup.lub.lu.se/record/2403156URI
  • https://doi.org/10.1016/j.artmed.2011.11.001DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

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

Anmärkningar

  • Objective: Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. Methods: When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. Results: The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. Conclusion: For clinical data consisting of variables of different types, the proposed kernel function which takes into account the type and range of each variable - has shown to be a better alternative for linear and non-linear classification problems. (C) 2011 Elsevier B.V. All rights reserved.

Ämnesord och genrebeteckningar

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

  • Timmerman, Dirk (författare)
  • Van den Bosch, Thierry (författare)
  • Bottomley, Cecilia (författare)
  • Kirk, Emma (författare)
  • Van Holsbeke, Caroline (författare)
  • Valentin, LilLund University,Lunds universitet,Obstetrisk, gynekologisk och prenatal ultraljudsdiagnostik,Forskargrupper vid Lunds universitet,Obstetric, Gynaecological and Prenatal Ultrasound Research,Lund University Research Groups(Swepub:lu)obst-lva (författare)
  • Bourne, Tom (författare)
  • De Moor, Bart (författare)
  • Obstetrisk, gynekologisk och prenatal ultraljudsdiagnostikForskargrupper vid Lunds universitet (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Artificial Intelligence in Medicine: Elsevier BV54:2, s. 103-1141873-28600933-3657

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