title: Developing computational model-based diagnostics to analyse clinical chemistry data.
abstract: This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data. These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large amounts of data that are difficult to interpret at first sight, but have high diagnostic potential. Computational models aid interpretation and make the data accessible for clinical diagnosis. We discuss the issues that a modeller has to take into account during the design, construction and evaluation phases of model development. We use the example of Particle Profiler development, a model-based diagnostic tool for lipoprotein disorders, as a case study, to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring and quality control.
authors.affiliation: U TNO Quality of Life Research Institute and at Leiden Amsterdam Centre for Drug Research at Leiden University. daan.vanschalkwijk@tno.nl
authors.forename: Daniël B
authors.initials: DB
authors.lastname: van Schalkwijk
authors.forename: Kees
authors.initials: K
authors.lastname: van Bochove
authors.forename: Ben
authors.initials: B
authors.lastname: van Ommen
authors.forename: Andreas P
authors.initials: AP
authors.lastname: Freidig
authors.forename: Eugene P
authors.initials: EP
authors.lastname: van Someren
authors.forename: Jan
authors.initials: J
authors.lastname: van der Greef
authors.forename: Albert A
authors.initials: AA
authors.lastname: de Graaf
mesh_terms: D003198:Computer Simulation; D003933:Diagnosis; D006801:Humans