Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies–Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as ‘witnessed e-cigarette use in their household’ and ‘perception of their tobacco use’, could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers

Nurses’ Interpretation of Patient Status Descriptions on the Braden Scale

The risk of pressure ulcers is widely assessed using the Braden Scale for Predicting Pressure Ulcer Risk, which describes patient characteristics for various severity levels. However, many of these characteristics are described in vague terms that nurses may interpret inconsistently, potentially threatening scale reliability. To examine the consistency of nurses\u27 interpretations of five vaguely described patient characteristics on the Braden Scale we surveyed a convenience sample of 102 nurses and compared their interpretations with those of two nurse experts. The results show large variations in nurses\u27 interpretations. Although the highest frequency of nurses\u27 responses to the majority of descriptions was consistent with the experts\u27 interpretation, the large variation in responses may seriously threaten consistent and accurate assessment of pressure-ulcer risk with the Braden Scale. Our findings suggest that training programs provide operational definitions of these vague patient descriptions, so the Braden Scale can be used consistently in patient care

sj-docx-1-jtt-10.1177_1357633X231167613 - Supplemental material for Artificial intelligence assisted telehealth for nursing: A scoping review

Supplemental material, sj-docx-1-jtt-10.1177_1357633X231167613 for Artificial intelligence assisted telehealth for nursing: A scoping review by Jeeyae Choi, Seoyoon Woo and Anastasiya Ferrell in Journal of Telemedicine and Telecare</p