R.I.P.
๐ป
Ghosted
Artificial Intelligence for In Silico Clinical Trials: A Review
September 16, 2022 ยท The Cartographer ยท ๐ arXiv.org
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Artificial Intelligence for In Silico Clinical Trials: A Review"
Evidence collected by the PWNC Scanner
Authors
Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun
arXiv ID
2209.09023
Category
q-bio.QM
Cross-listed
cs.AI,
cs.LG
Citations
16
Venue
arXiv.org
Last Checked
10 days ago
Abstract
A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ q-bio.QM
R.I.P.
๐ป
Ghosted
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
R.I.P.
๐ป
Ghosted
ProtVec: A Continuous Distributed Representation of Biological Sequences
R.I.P.
๐ป
Ghosted
A Perspective on Deep Imaging
R.I.P.
๐
404 Not Found
Deep learning in bioinformatics: introduction, application, and perspective in big data era
R.I.P.
๐ป
Ghosted