Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation.
This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing...
| Publicado en: | CIN: Computers, Informatics, Nursing Vol. 43; no. 5; pp. 1 - 7 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184974697&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184974697 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15382931 KXN jtl: CIN: Computers, Informatics, Nursing issn: 15382931 maglogo: N pubinfo: dt: May2025 vid: 43 iid: 5 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 184974697 184974697 184974697 10.1097/CIN.0000000000001277 184974697 ppf: 1 ppct: 6 formats: tig: atl: Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation. aug: au: Macrì, Davide Ramacciati, Nicola Comito, Carmela Metlichin, Elisabetta Giusti, Gian Domenico Forestiero, Agostino affil: Author Affiliations: Istituto di Calcolo e Reti ad Alte Prestazioni (Institute for High-Performance Computing and Networking) (Drs Macrì, Comito, and Forestiero) sug: subj: Chronic Pain Diagnosis Nursing Diagnosis Evaluation Quality Improvement Machine Learning Algorithms Evaluation Chronic Pain Classification Nursing Records Italy Health Care Delivery Italy Human Artificial Intelligence Italy Validation Studies Decision Making, Clinical Quality Assessment Sensitivity and Specificity Natural Language Processing Nursing Care Chronic Pain Nursing Nursing Informatics ab: This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms—XGBoost, gradient boosting, and BERT—were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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