Avoiding Pitfalls in Applying Prediction Models, As Illustrated by the Example of Prostate Cancer Diagnosis | Zendy

Henning Cammann | Zendy; Klaus Jung | Zendy; Hellmuth-A. Meyer | Zendy; Carsten Stephan | Zendy

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Avoiding Pitfalls in Applying Prediction Models, As Illustrated by the Example of Prostate Cancer Diagnosis

Author(s) -

Henning Cammann,

Klaus Jung,

Hellmuth-A. Meyer,

Carsten Stephan

Publication year - 2011

Publication title -

clinical chemistry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.705

H-Index - 218

eISSN - 1530-8561

pISSN - 0009-9147

DOI - 10.1373/clinchem.2011.166959

Subject(s) - computer science , context (archaeology) , medical diagnosis , prostate cancer , predictive modelling , field (mathematics) , calibration , population , multivariate statistics , risk analysis (engineering) , machine learning , data mining , artificial intelligence , medicine , statistics , cancer , mathematics , pathology , paleontology , environmental health , pure mathematics , biology

The use of different mathematical models to support medical decisions is accompanied by increasing uncertainties when they are applied in practice. Using prostate cancer (PCa) risk models as an example, we recommend requirements for model development and draw attention to possible pitfalls so as to avoid the uncritical use of these models.

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