The DOI matches its Crossref record; 93 of the 94 references it cites were found in a registry or an index.
Diagnostic and prognostic prediction models
Open accessAbstract
Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).
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“Bias can be categorized as statistical or social, and it can lead to unequal outcomes and hinder equitable healthcare provision [53], [54].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [53]
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Bias in artificial intelligence algorithms and recommendations for mitigation
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Metadata
- Authors
- Hendriksen, J. M. T., Geersing, G. J., Moons, K. G. M., & de Groot, J. A. H.
- Affiliations
- University Medical Center Utrecht (NL)
- Corresponding author
- Moons, K.G.M.
- Venue
- Journal of Thrombosis and Haemostasis
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 11
- Pages
- 129-141
- ISSN
- 1538-7836 (ISSN-L)
- OpenAlex
- W2171370925
- Cited by
- 229
- References
- 94
- Citations by year
- 2026: 15 · 2025: 18 · 2024: 15 · 2023: 20 · 2022: 26 · 2021: 23 · 2020: 36 · 2019: 17 · 2018: 14 · 2017: 15 · 2016: 11 · 2015: 11 · 2014: 7 · 2013: 1
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Thrombosis and Haemostasis (publishedVersion)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Internal Medicine › Venous Thromboembolism Diagnosis and Management
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- risk prediction model, diagnostic model, prognostic model, venous thromboembolism, deep vein thrombosis, pulmonary embolism, Wells score, model validation, discrimination (statistics), calibration, impact assessment, clinical decision support
- MeSH terms
- Humans, Models, Theoretical, Prognosis, Venous Thromboembolism
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