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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
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The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
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Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
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Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation.

Davide Macrì1, Nicola Ramacciati, Carmela Comito

  • 1Author Affiliations: Istituto di Calcolo e Reti ad Alte Prestazioni (Institute for High-Performance Computing and Networking) (Drs Macrì, Comito, and Forestiero); and Department of Pharmacy, Health and Nutritional Sciences, Università della Calabria (Dr Ramacciati), Rende, Cosenza; Residenze Protette Cerreto d'Esi (Residential Care Facility), Kursana lunga vita Coop. Soc. ONLUS, Cerreto d'Esi, Ancona (Dr Metlichin); and Nursing School, University of Perugia (Dr Giusti); and Servizio Formazione e Qualità, Azienda Ospedaliera di Perugia (Dr Giusti), Perugia, Italy.

Computers, Informatics, Nursing : CIN
|March 20, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning effectively classifies chronic pain in Italian nursing notes. XGBoost outperformed other algorithms, showing AI

Keywords:
Deep learningGradient boostingMachine learningNursing diagnosisNursing notesPrediction models

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Chronic pain classification is crucial for effective patient care.
  • Integrating Artificial Intelligence (AI) into healthcare can enhance clinical decision-making.
  • Processing Italian medical language presents unique challenges for AI models.

Purpose of the Study:

  • To evaluate machine learning algorithms for chronic pain classification using Italian nursing notes.
  • To validate nursing diagnoses of chronic pain.
  • To explore AI's potential in Italian healthcare settings.

Main Methods:

  • Three machine learning algorithms (XGBoost, gradient boosting, BERT) were optimized using grid search.
  • Hyperparameter tuning was performed for each model.
  • Algorithm performance was compared using Cohen's κ coefficient.

Main Results:

  • XGBoost demonstrated superior performance in classifying chronic pain.
  • BERT showed potential for handling complex Italian language structures.
  • Limitations included data volume and domain specificity for BERT.

Conclusions:

  • Algorithm selection is critical for successful clinical AI applications.
  • Machine learning holds significant potential for improving healthcare in Italy.
  • Future work should consider multimodal data for enhanced chronic pain classification.