Health & Care

[ad_1] Abstract Objective This study aims to develop a transformer-based deep learning model for real-time activity recognition and fall detection, addressing the limitations of existing methods in terms of accuracy and real-time applicability. Methods The proposed system uses sliding window segmentation technique to process wearable sensor data, including accelerometer, gyroscope and orientation signals. The transformer

[ad_1] Discussion This study developed and validated a predictive model for SPIs using retrospective data from 931 surgical patients under general anaesthesia. The sample size was determined based on practical considerations, using all eligible cases available during the study period, as a priori power calculation was not feasible in this retrospective design. To assess the

[ad_1] Discussion Our study provides a current snapshot of the extent to which hospitals in Victoria, Australia are engaged in the use of EHRs and the extent to which EHR data are used to support HAI surveillance. Vast heterogeneity was described in EHR functionalities, how routine care data were captured, and local approaches to reduce

[ad_1] Discussion The current systematic review highlighted the essential use cases of PSMSs towards improving patient safety within hospital settings. Notably, the majority of studies concentrated on drug safety and hospital infections accounting for 37.5% and 20.8% of the reviewed literature, respectively. This may imply a higher perceived risk and prevalence of issues in these

[ad_1] Discussion The results obtained in this study highlight the potential of AI for early detection of sepsis in the ED using free text analysis of patient concerns prior to clinical assessment. The random forest classifier demonstrated the best predictive performance among the models evaluated, with an AUPRC of 0.7890 and an AUC of 0.8000.