Impact of word embedding models on text analytics in deep learning environment: a review.

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

Flight of the COVID-19 patient: experience with a Wuhan evacuee: a case report.

BACKGROUND: Coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2, was declared a global pandemic by the World Health Organization in March 2020. CASE PRESENTATION: We report a case of a 51-year-old Chinese woman who was evacuated from Wuhan, China and diagnosed with coronavirus disease 2019 infection at a Southern California quarantine facility. Her clinical course was notable for high fevers, night sweats, productive cough, transient leukopenia, lymphopenia, thrombocytopenia, and transaminitis. Evolving hypoxia and infiltrates on chest imaging warranted the trial of an investigational antiviral drug - remdesivir. Our patient recovered and was discharged after 2 weeks of hospitalization. CONCLUSIONS: This case highlights our patient's clinical course, including diagnostic work-up, medical management, and challenges in defining non-infectivity in a relatively unknown disease.

Clinical care of chimeric antigen receptor T-cell patients and managing immune-related adverse effects in the ambulatory and hospitalized setting: a review.

Chimeric antigen receptor (CAR) T-cell therapies are increasingly providing options for care of oncology patients with advanced hematologic malignancies, which has led to two US FDA approvals. However, they are often associated with significant immune related adverse events that require prompt management. These toxicities are mainly cytokine release syndrome and neurotoxicity, and can be managed in an appropriate setting when presenting to nononcologists or internists. This paper discusses patient management for these toxicities. A management approach can be determined by the severity of the toxicity. Tocilizumab, a humanized monoclonal antibody, was FDA approved for the treatment of cytokine release syndrome, and corticosteroids may be used. Neurotoxicity is generally managed with supportive care and steroidal therapy.