| PharmaSUG |
2026 |
Streamlining Workflows in Real-World Evidence Studies with an R-Based Automation Tool |
Lihai Song |
source |
16 |
| PharmaSUG |
2025 |
Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) |
Felix Just, Krishna Padmanabhan, Parth Jinger, Amanda Borrow, Rüdiger Smolnik, Eva-Maria Fronk, Neelam Yadav, Sergei Krivtcov, Stas Zakharkin, Martin Unverdorben, Asmir Vodencarevic |
source |
6 |
| PHUSE US |
2025 |
Enhancing Clinical Trial Data Queries with LLMs and Neo4j: A Flexible Framework for ADaM Dataset Management |
Jaime Yan |
source presentation source |
28 |
| PHUSE US |
2024 |
Case study of verification and validation methods for algorithmic predictive biomarkers |
Kartik Shetty |
source presentation source |
4 |
| PHUSE US |
2024 |
Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data |
Kostiantyn Drach, Sergey Glushakov |
source presentation source |
12 |
| WUSS |
2023 |
What's next after model comparison? Model selection and model averaging in SAS |
Chong Ho Yu, Charlene Yang |
source |
14 |
| PHUSE EU |
2021 |
Deep Active Learning for Clinical Entity Recognition [presentation] |
Nadia Abraham |
source video |
33 |
| SGF |
2020 |
Machine Learning Approach to Combat the Opioid Epidemic |
Navjot Kaur |
source |
15 |
| SGF |
2020 |
MOVIE REVIEWS: TO READ OR NOT TO READ! Spoiler Detection with Applied Machine Learning |
Sreejita Biswas |
source |
11 |
| PharmaSUG |
2020 |
How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process |
Mirai Kikawa, Yuichi Nakajima |
source |
9 |
| SGF |
2018 |
Analytics Applications in Targeted Marketing and Forecasting Demand: A Two-Stage Model |
Anurag Hardikar |
source |
7 |
| SGF |
2012 |
Constructing a Credit Risk Scorecard Using Predictive Clusters |
Andres F. Gonzalez, Alejandro Correa Bahnsen, Ana C. Nieto, Darwin A. Amezquita |
source |
13 |