Healthcare professionals, just like any other community, can exhibit implicit biases. These biases adversely impact patients’ health outcomes. Promoting awareness of both social determinants of health (SDH) and the impact of implicit/explicit biases assists healthcare professionals to understand their patients well and improve care experiences. In addition, it helps to augment the long-lasting empathy and compassion in healthcare professionals towards patients for care treatments while maintaining better healthcare professional-patient relationships. Thus, this research provides Computer-Supported Expert-Guided Experiential Learning (CSEGEL) tools or mobile applications that facilitate healthcare professionals with a first-person learning experience to augment advanced healthcare skills (e.g., professional communication, cultural humility, awareness of both SDH and impact of biases on health outcomes). The CSEGEL tools in the form of mobile applications incorporate virtual reality-based serious role-playing scenarios along with a novel Life Course module to deliver first-person experiential learning capability to augment the advanced healthcare skills of healthcare professionals and public awareness. Finally, a preliminary data analysis is provided to demonstrate the positive influence of CSEGEL tools and measure the required number of sample sizes for concrete evidence to show effective results.
The COVID-19 virus induces infection in both the upper respiratory tract and the lungs. Chest X-ray are widely used to diagnose various lung diseases. Considering chest X-ray and CT images, we explore deep-learning-based models namely: AlexNet, VGG16, VGG19, Resnet50, and Resnet101v2 to classify images representing COVID-19 infection and normal health situation. We analyze and present the impact of transfer learning, normalization, resizing, augmentation, and shuffling on the performance of these models. We explored the vision transformer (ViT) model to classify the CXR images. The ViT model incorporates multi-headed attention to disclose more global information in constrast to CNN models at lower layers. This mechanism leads to quantitatively diverse features. The ViT model renders consolidated intermediate representations considering the training data. For experimental analysis, we use two standard datasets and exploit performance metrics: accuracy, precision, recall, and F1-score. The ViT model, driven by self-attention mechanism and longrange context learning, outperforms other models.
This paper proposes a novel information visualisation interface to help with the reading and improvement of biochips.The interface serves two main groups of end users.These are bio-chip model users and bio-chip model developers.Bio-chip model users are biologists who use the software to read chips and detect biochemical substances.Bio-chip model developers use the software to design and train classification models by seeing how well the different biosensors work and how well the data fits their model.The interface proposed uses a Random Forest classifier and visualises the classification to provide a better understanding of how the data is classified by showing how it fits different classifications and how changes in attribute values can affect the classification.The interface also allows model-developers to interact to see how their model works for different attribute values, and shows them how new data (sent by model-users) fits into their classification model.This allow the biochip designers to detect how their model may be limited so they can retrain the model accordingly.The particular challenge with this project is how we manage and visualise uncertainty related to bio-sensor readings (that can be resultant from the manufacturing process and environmental factors) and the machine learning models, so that biologists can account for this when designing or using chips.Overall, our interface demonstrates the potential of information visualisation to be used to allow developers and model-users to better understand the effectiveness of classification models for their data, as well as the potential of collaborative interfaces to help them work together to build more effective supervised classification models.
Care experiences and health outcomes may suffer greatly because of healthcare professionals' deficient educational preparation and practices. The limited awareness about the impact of stereotypes, implicit/explicit biases, and social determinants of health (SDH) may result in unpleasant care experiences and healthcare professional-patient relationships. Additionally, as healthcare professionals are no less prone to have biases than other people, it is essential to deliver the learning platform to enhance healthcare skills (e.g., awareness of the importance of cultural humility, inclusive communication proficiencies, awareness of the enduring impact of both SDH and implicit/explicit biases on health outcomes, and compassionate and empathetic attitude) of healthcare professionals, which eventually help to raise health equity in society. Moreover, employing the "learning-by-doing" approach directly in real-life clinical practices is less preferable wherein high-risk care is essential. Thus, there is a huge scope to deliver virtual reality-based care practices by engaging the digital experiential learning and human-computer interaction (HCI) approach to enhance patient care experiences, healthcare experiences, and healthcare skills. Thus, this research provides the computer-supported experiential learning (CSEL) approach-based tool or mobile application that facilitates virtual reality-based serious role-playing scenarios to enhance the healthcare skills of healthcare professionals and for public awareness.
We present a new web-based, client-server data processing and visualization framework that supports a flexible workflow, enabling the user to customize different data processing and visualization tasks with tools implemented in different programming languages. Our framework supports server-side applications developed with different languages, allowing visualization researchers to easily make their new techniques available to the target users. The client-side of our framework is implemented in the web browser environment with customizable interface and visualizations. We describe the design of the architecture of our framework and the process of adding new user-defined tasks, followed by the demonstration of the proposed framework on a number of data processing and visualization tasks.
