title: Improved modeling of clinical data with kernel methods.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors: Department of Electrical Engineering, Katholieke Universiteit Leuven, and Department of Obstetrics and Gynecology, University Hospitals Leuven, 3001 Leuven, Belgium. anneleen.daemen@gmail.com
authors: Anneleen
authors: A
authors: Daemen
authors: Dirk
authors: D
authors: Timmerman
authors: Thierry
authors: T
authors: Van den Bosch
authors: Cecilia
authors: C
authors: Bottomley
authors: Emma
authors: E
authors: Kirk
authors: Caroline
authors: C
authors: Van Holsbeke
authors: Lil
authors: L
authors: Valentin
authors: Tom
authors: T
authors: Bourne
authors: Bart
authors: B
authors: De Moor
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001185:Artificial Intelligence; D056808:Biostatistics; D001943:Breast Neoplasms / Q000175:diagnosis / Q000628:therapy; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005260:Female; D006176:Gynecology; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D017711:Nonlinear Dynamics; D011247:Pregnancy; D011379:Prognosis; D060388:Support Vector Machine; D055815:Young Adult