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Nursing Assessment of the Genitourinary System I: Health History01:21

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The genitourinary system is critical to maintaining fluid balance, waste elimination, and reproductive function. Nurses play a vital role in assessing this system, beginning with a thorough health history. This process involves gathering patient information, identifying risk factors, and recognizing symptoms of genitourinary disorders. Early detection is vital for timely interventions and management.1. Gathering Patient InformationA complete health history includes the patient’s personal,...
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The nursing assessment of the genitourinary (GU) system involves a systematic inspection and palpation to identify abnormalities in the kidneys, bladder, and surrounding structures.InspectionMouth: Inspect for signs of kidney dysfunction, such as stomatitis (inflammation of the mouth) and ammonia breath, which may occur in advanced kidney disease due to the buildup of urea, breaking down into ammonia.Skin: Check for pallor, which could indicate anemia caused by kidney disease. Look for...
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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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The genitourinary system maintains the body's fluid balance, waste excretion, and overall homeostasis. Proper assessment is essential for early detection of disorders, with percussion and auscultation integral to this evaluation. These methods help identify signs of kidney or bladder issues and provide important diagnostic clues.Percussion for Kidney TendernessPercussion is used to assess tenderness and detect kidney and bladder abnormalities. A common method for determining kidney tenderness...
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Urinalysis is a widely used diagnostic test that analyzes urine's physical, chemical, and microscopic characteristics. Healthcare providers use it to detect and monitor various health conditions, including renal disease, urinary tract infections (UTIs), diabetes, and metabolic or systemic disorders.Components of UrinalysisUrinalysis consists of three primary components: physical, chemical, and microscopic examination. Each provides unique insights into the urine sample and, by extension, the...
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Utilizing Open-Source Large Language Models to Extract Genitourinary Symptoms from Clinical Notes.

Yunbing Bai1, Wanting Cui1, Joseph Finkelstein1

  • 1Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah.

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Summary

This study shows that the Llama 3.3-70B model accurately extracts genitourinary signs and symptoms (S&S) and generates ICD-10 codes from clinical notes. Optimal performance was achieved using specific prompts with predefined ICD-10 definitions.

Keywords:
Large Language ModelsLlama ModelsNatural Language ProcessingSymptom Extraction

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Data Extraction

Background:

  • Accurate identification of patient signs and symptoms (S&S) from clinical notes is crucial for diagnosis, treatment, and research.
  • Urological clinical notes contain valuable information for understanding genitourinary conditions.
  • Large language models (LLMs) show potential for automating clinical data extraction.

Purpose of the Study:

  • To evaluate the performance of the Meta Llama model in extracting genitourinary signs and symptoms and their corresponding ICD-10 codes from clinical notes.
  • To compare LLM extraction results against manually annotated data.
  • To identify optimal prompting strategies for enhancing LLM accuracy in clinical text analysis.

Main Methods:

  • Utilized the Llama 3.3-70B model for text analysis.
  • Employed prompt engineering techniques, including providing predefined ICD-10 code definitions and restricting model assumptions.
  • Evaluated performance on the MTSamples dataset containing urological clinical notes, with manual annotation for ground truth.
  • Measured performance using recall, precision, and F1-score for both S&S extraction and ICD-10 code generation.

Main Results:

  • Llama 3.3-70B demonstrated high performance in extracting genitourinary signs and symptoms (S&S) and generating ICD-10 codes.
  • The best results were achieved when prompts included predefined ICD-10 code definitions and disallowed model assumptions.
  • Achieved an average recall of 0.96, precision of 0.89, and F1-score of 0.92 for S&S extraction.
  • Achieved an average recall of 0.93, precision of 0.85, and F1-score of 0.89 for ICD-10 code generation.

Conclusions:

  • The Llama 3.3-70B model, with optimized prompting, is effective for extracting genitourinary signs and symptoms and associated ICD-10 codes from clinical notes.
  • Careful prompt engineering, including providing context and constraints, significantly improves LLM performance in specialized medical domains.
  • This approach holds promise for improving the efficiency and accuracy of clinical data analysis and research.