CT-ADE: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
- URL: http://arxiv.org/abs/2404.12827v1
- Date: Fri, 19 Apr 2024 12:04:32 GMT
- Title: CT-ADE: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
- Authors: Anthony Yazdani, Alban Bornet, Boya Zhang, Philipp Khlebnikov, Poorya Amini, Douglas Teodoro,
- Abstract summary: Adverse drug events (ADEs) significantly impact clinical research and public health.
We introduce CT-ADE, a novel dataset compiled to enhance the predictive modeling of ADEs.
- Score: 0.10555513406636093
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design.
We provide basic validation methods for each task to ensure the datasets' usability and reliability.
We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z) - Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
A major challenge for AI-driven drug discovery is the scarcity of high-quality data.
We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously.
Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
arXiv Detail & Related papers (2024-04-16T07:42:55Z) - Enhancing Acute Kidney Injury Prediction through Integration of Drug
Features in Intensive Care Units [0.0]
The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs has yet to be explored in the critical care setting.
This study proposes a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.
arXiv Detail & Related papers (2024-01-09T05:42:32Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic
Tree-Based Memory Network [54.332862955411656]
Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment.
In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials.
We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
arXiv Detail & Related papers (2023-07-19T12:35:09Z) - Prediction of drug effectiveness in rheumatoid arthritis patients based
on machine learning algorithms [2.5759046095742453]
Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue.
Machine learning (ML) has the potential to identify patterns in patient electronic health records to forecast the best clinical treatment to improve patient outcomes.
This study introduced a Drug Response Prediction (TNF) framework with two main goals: 1) design a data processing pipeline to extract information from clinical data, and then preprocess it for functional use, and 2) predict RA patient's responses to drugs and evaluate classification models' performance.
arXiv Detail & Related papers (2022-10-14T15:15:37Z) - Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
We introduce techniques to help bridge this gap between EBM and AI-based clinical models.
We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information.
Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
arXiv Detail & Related papers (2021-11-16T11:19:02Z) - HINT: Hierarchical Interaction Network for Trial Outcome Prediction
Leveraging Web Data [56.53715632642495]
Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment.
In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
arXiv Detail & Related papers (2021-02-08T15:09:07Z) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z) - Patient ADE Risk Prediction through Hierarchical Time-Aware Neural
Network Using Claim Codes [5.288589720985491]
The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes.
We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship.
arXiv Detail & Related papers (2020-08-20T13:24:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.