no code implementations • 14 Oct 2024 • Luis H. John, Chungsoo Kim, Jan A. Kors, Junhyuk Chang, Hannah Morgan-Cooper, Priya Desai, Chao Pang, Peter R. Rijnbeek, Jenna M. Reps, Egill A. Fridgeirsson
Deep learning methods promise enhanced prediction performance by extracting complex patterns from clinical data, but face challenges like data sparsity and high dimensionality.
no code implementations • 14 Aug 2020 • Luis H. John, Jan A. Kors, Jenna M. Reps, Patrick B. Ryan, Peter R. Rijnbeek
Objective: Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing objectives of improving model performance and reducing model complexity as well as computational requirements.
no code implementations • 20 Jul 2016 • Jenna M. Reps, Uwe Aickelin, Richard B. Hubbard
We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms.
no code implementations • 20 Feb 2015 • Jenna M. Reps, Uwe Aickelin, Jiangang Ma, Yanchun Zhang
Side effects of prescribed medications are a common occurrence.
no code implementations • 3 Sep 2014 • Jenna M. Reps, Uwe Aickelin, Jonathan M. Garibaldi
The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier.
no code implementations • 2 Sep 2014 • Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.