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Found 6 papers, 0 papers with code
Comparison of deep learning and conventional methods for disease onset prediction
Deep learning methods promise enhanced prediction performance by extracting complex patterns from clinical data, but face challenges like data sparsity and high dimensionality.
Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
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.
Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining
We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms.
Refining Adverse Drug Reactions using Association Rule Mining for Electronic Healthcare Data
Side effects of prescribed medications are a common occurrence.
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
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.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
