Abstract
In generating real world data (RWD), machine learning (ML) extraction of clinical characteristics from unstructured text (e.g. clinical notes) in electronic health records (EHRs) is more cost-effective and scalable than manual abstraction. Proper evaluation that goes beyond standard ML metrics is needed to determine whether ML-extracted variables are fit for research use [1]. This study evaluates reproducibility of scientific conclusions when using expert-abstracted versus ML-extracted data in comparing the effectiveness on real-world overall survival (rwOS) of bevacizumab-carboplatin-paclitaxel (BCP) versus carboplatin-paclitaxel (CP) for first-line treatment of non-squamous metastatic non-small cell lung cancer (mNSCLC).
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