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Add to EdgeCT-ADE: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
Apr 19, 2024
Adverse drug events (ADEs) significantly impact clinical research and public health, contributing to failures in clinical trials and leading to increased healthcare costs. The accurate prediction and management of ADEs are crucial for improving the development of safer, more effective medications, and enhancing patient outcomes. To support this effort, we introduce CT-ADE, a novel dataset compiled to enhance the predictive modeling of ADEs. Encompassing over 12,000 instances extracted from clinical trial results, the CT-ADE dataset integrates drug, patient population, and contextual information for multilabel ADE classification tasks in monopharmacy treatments, providing a comprehensive resource for developing advanced predictive models. To mirror the complex nature of ADEs, annotations are standardized at the system organ class level of the Medical Dictionary for Regulatory Activities (MedDRA) ontology. Preliminary analyses using baseline models have demonstrated promising results, achieving 73.33% F1 score and 81.54% balanced accuracy, highlighting CT-ADE's potential to advance ADE prediction. CT-ADE provides an essential tool for researchers aiming to leverage the power of artificial intelligence and machine learning to enhance patient safety and minimize the impact of ADEs on pharmaceutical research and development. Researchers interested in using the CT-ADE dataset can find all necessary resources at https://github.com/xxxx/xxxx.
ProcNet: Deep Predictive Coding Model for Robust-to-occlusion Visual Segmentation and Pose Estimation
Oct 27, 2023Systems involving human-robot collaboration necessarily require that steps be taken to ensure safety of the participating human. This is usually achievable if accurate, reliable estimates of the human's pose are available. In this paper, we present a deep Predictive Coding (PC) model supporting visual segmentation, which we extend to pursue pose estimation. The model is designed to offer robustness to the type of transient occlusion naturally occurring when human and robot are operating in close proximity to one another. Impact on performance of relevant model parameters is assessed, and comparison to an alternate pose estimation model (NVIDIA's PoseCNN) illustrates efficacy of the proposed approach.