Model predicts drug overdose hotspots using EMS and toxicology data.
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5 results for “toxicology”
problem Predicting drug overdose hotspots to focus limited services.
method Spatial-temporal point process model integrating EMS and toxicology data.
result Model improves prediction accuracy by integrating heterogeneous data.
Bayesian framework improves minority class performance in class-imbalanced data.
problem Class imbalance in predictive toxicology models.
method Weighted likelihood approach modifying likelihood function weights inversely proportional to class proportions.
result Improves balanced accuracy and sensitivity for minority class (toxic compounds).
Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA …
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
problem Complexity and limited data in modeling cardiac effects of drugs.
method Combining meta-learning with SBINNs to solve parameterized cardiac action potential models.
result hyperSBINN outperforms traditional solvers in speed and accuracy for predicting APD90 values.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.