Name
Abstract | ||
|---|---|---|
Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as
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Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for
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Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2019.2949888 | IEEE journal of biomedical and health informatics |
Keywords | DocType | Volume |
Biological system modeling,Numerical models,Computational modeling,Predictive models,Mathematical model,Uncertainty,Analytical models | Journal | 24 |
Issue | ISSN | Citations |
1 | 2168-2194 | 1 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Viceconti | 1 | 22 | 7.23 |
| Miguel Juarez | 2 | 3 | 1.16 |
| Cristina Curreli | 3 | 1 | 1.06 |
| Marzio Pennisi | 4 | 109 | 23.03 |
| Giulia Russo | 5 | 15 | 10.89 |
| F. Pappalardo | 6 | 76 | 20.14 |