Natural Language Processing to Extract Head and Neck Cancer Data From Unstructured Electronic Health Records.

Journal: Clinical oncology (Royal College of Radiologists (Great Britain))
PMID: 40188745

Abstract

AIMS: Patient data is frequently stored as unstructured data within Electronic Health Records (EHRs), requiring manual curation. AI tools using Natural Language Processing (NLP) may rapidly curate accurate real-world unstructured EHRs to enrich datasets. We evaluated this approach for Head and Neck Cancer (HNC) patient data extraction using an open-source general-purpose healthcare NLP tool (CogStack).

Authors

  • T Young
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK. Electronic address: thomas.young@gstt.nhs.uk.
  • J Au Yeung
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • K Sambasivan
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • D Adjogatse
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • A Kong
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • I Petkar
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • M Reis Ferreira
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • M Lei
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • A King
    King's College London, UK.
  • J Teo
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.
  • T Guerrero Urbano
    Guy's and St Thomas' NHS Foundation Trust (GSTT), UK; King's College London, UK.

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