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Related Experiment Video

Updated: Sep 5, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A

Melissa Estevez1, Corey M Benedum1, Chengsheng Jiang1

  • 1Flatiron Health, Inc., 233 Spring Street, New York, NY 10013, USA.

Cancers
|July 9, 2022
PubMed
Summary

Extracting valuable patient data from electronic health records (EHRs) using Natural Language Processing (NLP) and Machine Learning (ML) is challenging. We propose a framework to evaluate ML-extracted real-world data (RWD) for reliable research.

Keywords:
artificial intelligencedeep learningmachine learningoncologypersonalized medicine

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Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Biomedical Data Science

Background:

  • Electronic health records (EHRs) contain vast unstructured text data (e.g., pathology reports, clinical notes).
  • Manual extraction of this data for research is costly, time-consuming, and difficult to scale.
  • Natural Language Processing (NLP) and Machine Learning (ML) offer automated solutions for information extraction from EHRs.

Purpose of the Study:

  • To address the challenges in assessing the validity and generalizability of ML-extracted real-world data (RWD).
  • To propose a research-centric evaluation framework for ML models and their outputs.
  • To facilitate the effective and accurate use of ML-extracted EHR data for research and real-world evidence generation.

Main Methods:

  • Development of a novel evaluation framework tailored for ML-extracted RWD.
  • Focus on assessing data validity and generalizability across different patient cohorts.
  • Guidance for model developers, data users, and stakeholders in the RWD ecosystem.

Main Results:

  • The proposed framework provides a structured approach to evaluating ML-derived RWD.
  • It aims to enhance the reliability and applicability of RWD extracted from EHRs.
  • Facilitates better understanding and utilization of ML outputs in clinical research.

Conclusions:

  • A standardized evaluation framework is crucial for the trustworthy application of ML-extracted RWD in research.
  • This framework supports maximizing the utility of EHR data for generating real-world evidence.
  • Promotes robust and reproducible research using advanced data extraction techniques.