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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from

Sena Chae1, Jaewon Bae1, Pratik Maitra2

  • 1College of Nursing, The University of Iowa, Iowa City, Iowa (Chae, Bae, Dunn Lopez, and Rakel).

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Summary
This summary is machine-generated.

A new natural language processing (NLP) system accurately extracts patient symptoms from clinical notes. This tool aids in understanding symptom prevalence and documentation patterns in acute myeloid leukemia (AML) treatment.

Keywords:
Acute myeloid leukemia, Data mining, Symptom assessment

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Area of Science:

  • Medical Informatics
  • Oncology
  • Natural Language Processing

Background:

  • Patients with acute myeloid leukemia (AML) face severe, fluctuating symptoms during treatment.
  • Accurate symptom identification is crucial but hindered by underdocumentation and unstructured electronic health records.

Purpose of the Study:

  • Develop and validate a natural language processing (NLP) system for symptom extraction from clinical notes.
  • Characterize symptom prevalence, co-occurrence, and documentation patterns in AML patients.

Main Methods:

  • Analyzed 78,392 clinical notes from 812 AML patients (2006-2021).
  • Defined 10 symptom categories and validated NLP performance (average F1 = 0.90).
  • Examined documentation variations by patient demographics and provider type.

Main Results:

  • NLP system demonstrated high accuracy in symptom extraction.
  • Gastrointestinal, pain, and myelosuppression symptoms were most frequently documented.
  • Documentation patterns varied by patient sex, age, and healthcare provider.

Conclusions:

  • NLP facilitates accurate, scalable symptom data extraction from unstructured notes for AML surveillance and predictive modeling.
  • Findings underscore the need for standardized documentation and tailored interventions for symptom management.