OBJECTIVES: Well-designed electronic health records (EHRs) training programs for clinical practice are known to be valuable. Training programs should be role-specific and there is a need to identify key implementation factors of EHR training programs for nurses. This scoping review (1) characterizes the EHR training programs used and (2) identifies their implementation facilitators and barriers. MATERIALS AND METHODS: We searched MEDLINE, CINAHL, PsycINFO, and Web of Science on September 3, 2023, for peer-reviewed articles that described EHR training program implementation or delivery to nurses in inpatient settings without any date restrictions. We mapped implementation factors to the Consolidated Framework for Implementation Research. Additional themes were inductively identified by reviewing these findings. RESULTS: This review included 30 articles. Healthcare systems' approaches to implementing and delivering EHR training programs were highly varied. For implementation factors, we observed themes in innovation (eg, ability to practice EHR skills after training is over, personalizing training, training pace), inner setting (eg, availability of computers, clear documentation requirements and expectations), individual (eg, computer literacy, learning preferences), and implementation process (eg, trainers and support staff hold nursing backgrounds, establishing process for dissemination of EHR updates). No themes in the outer setting were observed. DISCUSSION: We found that multilevel factors can influence the implementation and delivery of EHR training programs for inpatient nurses. Several areas for future research were identified, such as evaluating nurse preceptorship models and developing training programs for ongoing EHR training (eg, in response to new EHR workflows or features). CONCLUSIONS: This scoping review highlighted numerous factors pertaining to training interventions, healthcare systems, and implementation approaches. Meanwhile, it is unclear how external factors outside of a healthcare system influence EHR training programs. Additional studies are needed that focus on EHR retraining programs, comparing outcomes of different training models, and how to effectively disseminate updates with the EHR to nurses.

Virtual reality (VR) technologies have shown promising potential in the early diagnosis of dementia by enabling accessible and regular assessment. However, previous VR studies were restricted to the analysis of behavioral responses, so information about degenerated brain dynamics could not be directly acquired. To address this issue, we provide a cognitive impairment (CI) screening tool based on a wearable EEG device integrated into a VR platform. Subjects were asked to use a hardware setup consisting of a frontal six-channel EEG device mounted on a VR device and to perform four cognitive tasks in VR. Behavioral response profiles and EEG features were extracted during the tasks, and classifiers were trained on extracted features to differentiate subjects with CI from healthy controls (HCs). Notably, the performance of the patient classification consistently improved when EEG characteristics measured during cognitive tasks were additionally included in feature attributes than when only the task scores or resting-state EEG features were used, suggesting that our protocol provides discriminative information for screening. These results propose that the integration of EEG devices into a VR framework could emerge as a powerful and synergistic strategy for constructing an easily accessible EEG-based CI screening tool.

Recent studies on deep learning for biomedical data analysis have shown that guiding neural networks (NNs) to incorporate appropriate domain knowledge can enhance model performance. Accordingly, we present an auxiliary classification task for sleep staging to enable NNs to exploit clinically significant EEG patterns in data. Specifically, representations constructed from a global max pooling operator, promoting the NNs to extract distinctive signal patterns for each sleep stage, are adopted herein. While previous studies required additional training processes or parameter tuning phases for correct formulation of the domain knowledge during training, our method operates in an end-to-end manner without further calibration. During the experiments, the proposed method improved the classification accuracies compared to the original model. Moreover, intermediate representations in the proposed method were highly interpretable, wherein medically-significant features were correctly addressed with the activation values. Overall, the proposed method successfully augmented model qualities in two important aspects for adoption of AI in medical tasks: good model performance and clinically sound interpretation of models.

MOTIVATION: Existing k-mer based string kernel methods have been successfully used for sequence comparison. However, existing kernel methods have limitations for comparative and evolutionary comparisons of genomes due to the sensitiveness to over-represented k-mers and variable sequence lengths. RESULTS: In this study, we propose a novel ranked k-spectrum string (RKSS) kernel. 1) RKSS kernel utilizes common k-mer sets across species, named landmarks, that can be used for comparing multiple genomes. 2) Based on the landmarks, we can use ranks of k-mers, rather than frequencies, that can produce more robust distances between genomes. To show the power of RKSS kernel, we conducted two experiments using 10 mammalian species with exon, intron, and CpG island sequences. RKSS kernel reconstructed more consistent evolutionary trees than the k-spectrum string kernel. In the subsequent experiment, for each sequence, kernel distance was calculated from 30 landmarks representing exon, intron, and CpG island sequences of 10 genomes. Based on kernel distances, concordance tests were performed and the result suggested that more information is conserved in CpG islands across species than in introns. In conclusion, our analysis suggests that the relational order, exon CpG island intron, in terms of evolutionary information contents.

MOTIVATION: Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification. RESULTS: We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.