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 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.

Using a Mobile Application to Promote Patient Education for Patients With Liver Cirrhosis

Patient education and self-management are essential for patients with liver cirrhosis. Based on Fisher and Fisher's Information-Motivation-Behavior Skills model, a Cirrhosis Care App was developed to support the education and self-management of these patients. To evaluate the effectiveness of the application, a randomized controlled trial was conducted with patients having liver cirrhosis who were being followed up in the outpatient area of ​​a medical center in Taiwan. The experimental group used the app for 1 month, whereas a control group continued to receive conventional patient education. A pretest and posttest questionnaire was used to evaluate the app's effectiveness in improving the knowledge and practice of self-care. In addition, a questionnaire was developed based on the Technology Acceptance Model to understand satisfaction with the app. Results showed that following the implementation of the Cirrhosis Care App, patients' self-care knowledge and ability to promote self-care practice improved. User satisfaction with the app was measured and reflected in its frequency of use. This study confirmed that the Cirrhosis Care App, based on the Information-Motivation-Behavior Skills model, can improve patient knowledge and self-care practice and be actively promoted to benefit patients with cirrhosis.

Privacy-Preserving Cameras for Fall Detection: Data Acquisition for Artificial Intelligence

No abstract available