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Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.
Bhatia, Ansh; Titus, Renil; Porto, Joao G; Katz, Jonathan; Lopategui, Diana M; Marcovich, Robert; Parekh, Dipen J; Shah, Hemendra N.
Afiliación
  • Bhatia A; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA.
  • Titus R; Seth GS Medical College and King Edward Memorial Hospital, Mumbai, India.
  • Porto JG; Seth GS Medical College and King Edward Memorial Hospital, Mumbai, India.
  • Katz J; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA.
  • Lopategui DM; Department of Urology, University of California, San Diego, San Diego, California, USA.
  • Marcovich R; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA.
  • Parekh DJ; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA.
  • Shah HN; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, Florida, USA.
J Endourol ; 38(8): 852-864, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38613805
ABSTRACT

Introduction:

Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and

Methods:

We conducted a literature search of PubMed and Google Scholar databases using the keywords "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance.

Results:

Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation.

Conclusion:

NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Procesamiento de Lenguaje Natural / Registros Electrónicos de Salud Límite: Humans / Male Idioma: En Revista: J Endourol Asunto de la revista: UROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Procesamiento de Lenguaje Natural / Registros Electrónicos de Salud Límite: Humans / Male Idioma: En Revista: J Endourol Asunto de la revista: UROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos