Aaron B. Cohen

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework

2022 · 26 Zit. · Cancers

A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

2024 · 15 Zit. · Nature Communications

Approach to Machine Learning for Extraction of Real-World Data Variables from Electronic Health Records

2023 · 8 Zit. · medRxiv

Ensuring Reliability of Curated Electronic Health Record–Derived Data: The Validation of Accuracy for Large Language Model–/Machine Learning–Extracted Information and Data (VALID) Framework

2026 · 0 Zit. · JCO Clinical Cancer Informatics

SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations

2024 · 0 Zit. · arXiv (Cornell University)

Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework

2025 · 0 Zit. · ArXiv.org

SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations

2024 · 0 Zit. · Research Square