Immersive technology, such as virtual reality (VR), has become more integrated in children's lives transforming how they experience education, medical treatment, and entertainment. In VR, children are likely to engage with interactive and socially real characters. To examine children's experience of virtual characters in VR, we studied 5-to 9-year old's (N= 25) spontaneous reactions towards three virtual character types (human, anthropomorphized fictional Muppet, animal). Results showed children engaged in four major behavioral interactions: they tried to touch the characters, embodied the characters, talked directly to the characters, and referred to themselves in regard to the virtual environment. These results suggest that children test concepts of realism through touch and verbalizations and physically examine social boundaries. Additionally, children consider self-representation while in a virtual environment. We discuss the implications of these results for future work and provide design considerations when creating VR content with and for children.

Healthcare is transforming into a data-intensive industry with the expectation to double its own data every 73 days by 2020. Electronic Health Records hold a vast amount of information that has the potential of improving care delivery ranging from management tasks in hospitals to inferring diagnoses from X-ray images. The massive volume of data, such as demographic data, diagnoses, tests, prescribed medications, and procedures, can be used to predict health risk or diagnose diseases. But few pay attention to the medical notes which contain abundant and critical information written by healthcare service providers during a patient’s stay or visit to the hospital. Because of the unstructured feature in these notes, they are usually underutilized to build prediction models. This project incorporates medical notes (e.g., discharge notes) along with demographic data available in the MIMIC-III dataset, to visualize patterns and finally train a prediction model for readmission of patients in the ICU.

The outbreak of COVID-19 has a severe impact on our families, communities, and businesses. Researchers, practitioners, and administrators need a tool to help them digest this enormous amount of knowledge to address various scientific questions related to COVID-19. With CORD-19 dataset, this paper showcases the COVID-19 portal to portray the research profiles of scientists, bio entities (e.g., gene, drug, disease), and institutions based on the integration of CORD-19 research literature, COVID-19 related clinical trials, PubMed knowledge graph, and the drug discovery knowledge graph. This portal provides the following profiles related to COVID-19: 1) the profile of a research scientist with his/her COVID-19 related publications and clinical trials with tweets amount; 2) the profile of a bio entity which could be a gene, a drug, or a disease with articles and clinical trials; and 3) the profile of an institution with papers authored by researchers from this institution.