Artif Intell Med - Improved modeling of clinical data with kernel methods.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv despit rise highthroughput technolog clinic data age gender medic histori guid clinic manag diseas examin improv clinic manag avail patient inform fulli exploit requir appropri model relev parametersmethod kernel method use tradit kernel function linear kernel often appli set clinic paramet kernel function howev disadvantag due specif characterist clinic data mix variabl type variabl rang propos new kernel function specif adapt characterist clinic dataresult clinic kernel function provid better represent patient similar equal influenc variabl take account rang r variabl moreov robust respect chang r incorpor least squar support vector machin new kernel function result signific improv diagnosi prognosi predict therapi respons illustr four clinic data set within gynecolog averag increas test area roc curv auc respect moreov combin clinic paramet express data three case studi breast cancer result improv overal use new kernel function consid data type weight fashion larger weight assign clinic paramet increas auc respect standard kernel function andor unweight data combin maximum three case studiesconclus clinic data consist variabl differ type propos kernel functionwhich take account type rang variableha shown better altern linear nonlinear classif problem

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