Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study
Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a
major risk factor. We aimed to investigate the use of deep learning methods to predict the …
major risk factor. We aimed to investigate the use of deep learning methods to predict the …
Machine learning-based modeling of big clinical trials data for adverse outcome prediction: A case study of death events
It is known that clinical trials have potential risks for participants, which could result in
unexpected adverse events. To quantify and predict the risk of adverse outcomes, we …
unexpected adverse events. To quantify and predict the risk of adverse outcomes, we …
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Clinical trials are pivotal for developing new medical treatments, yet they typically pose
some risks such as patient mortality, adverse events, and enrollment failure that waste …
some risks such as patient mortality, adverse events, and enrollment failure that waste …
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
Despite many recent advances in systems biology and a marked increase in the availability
of high-throughput biological data, the productivity of research and development in the …
of high-throughput biological data, the productivity of research and development in the …
Improving clinical trial design using interpretable machine learning based prediction of early trial termination
E Kavalci, A Hartshorn - Scientific reports, 2023 - nature.com
This study proposes using a machine learning pipeline to optimise clinical trial design. The
goal is to predict early termination probability of clinical trials using machine learning …
goal is to predict early termination probability of clinical trials using machine learning …
Can machine learning augment clinician adjudication of events in cardiovascular trials? A case study of major adverse cardiovascular events (MACE) across CVRM …
H Lea, E Hutchinson, A Meeson… - European Heart …, 2021 - academic.oup.com
Background and introduction Accurate identification of clinical outcome events is critical to
obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for …
obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for …
Artificial intelligence in clinical trials
Overall, current clinical trial success rate is in the range of 10–13.8%. The oncology clinical
trial success rate range is even lower at 3.4–5.1%(Thomas et al., Clinical development …
trial success rate range is even lower at 3.4–5.1%(Thomas et al., Clinical development …
Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond
In a recent issue of Nature Machine Intelli-gence, Dhiman et al. 1 highlight the importance of
planning evaluations of deep learning systems in advance by predefining study protocols …
planning evaluations of deep learning systems in advance by predefining study protocols …
Application of machine learning methods in clinical trials for precision medicine
Objective A key component for precision medicine is a good prediction algorithm for patients'
response to treatments. We aim to implement machine learning (ML) algorithms into the …
response to treatments. We aim to implement machine learning (ML) algorithms into the …
Predictive modeling of clinical trial terminations using feature engineering and embedding learning
ME Elkin, X Zhu - Scientific reports, 2021 - nature.com
In this study, we propose to use machine learning to understand terminated clinical trials.
Our goal is to answer two fundamental questions:(1) what are common factors/markers …
Our goal is to answer two fundamental questions:(1) what are common factors/markers …