Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Summary

Researchers developed a machine learning tool that accurately extracts PD-L1 expression levels from unstructured clinical notes within electronic health records. This automation reduces the need for manual chart review and facilitates larger population-level studies on cancer immunotherapy response.

Key result

The natural language processing tool extracted PD-L1 positive labels with a mean precision of 0.859, recall of 0.994, and an F1 score of 0.921.

Abstract

Introduction: PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. Methods: We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. Results: The model demonstrated strong evaluation performance across multiple levels of label granularity. Mean precision of the overall PD-L1 positive label was 0.859 (sd, 0.039), recall 0.994 (sd, 0.013), and F1 0.921 (0.024). When a numeric PD-L1 value was identified, the mean absolute error of the value was 0.537 on a scale of 0 to 100. Conclusion: We presented an accurate NLP method for deriving PD-L1 status from clinical notes. By reducing the time and manual effort needed to review medical records, our work will enable future population-level studies in cancer immunotherapy.