A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare

Driven by the visions of Data Science, recent years have seen a paradigm shift in Natural Language Processing (NLP). NLP has set the milestone in text processing and proved to be the preferred choice for researchers in the healthcare domain. The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain, making data a critical and trusted component in improving the health outcomes. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain. Our findings suggest, first, the techniques of NLP those supporting knowledge management extraction and knowledge capture processes in healthcare. Second, we propose a conceptual model for the knowledge extraction process through NLP. Finally, we discuss a set of issues, challenges, and proposed future research areas

A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare

Driven by the visions of Data Science, recent years have seen a paradigm shift in Natural Language Processing (NLP). NLP has set the milestone in text processing and proved to be the preferred choice for researchers in the healthcare domain. The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain, making data a critical and trusted component in improving health outcomes. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain. Our findings suggest, first, the techniques of NLP those supporting knowledge management extraction and knowledge capture processes in healthcare. Second, we propose a conceptual model for the knowledge extraction process through NLP. Finally, we discuss a set of issues, challenges, and proposed future research areas

Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework

This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks. Selection criteria was based on journal ranking and impact factor. The research seeks to provide a valuable resource for future healthcare researchers, furnishing them with a comprehensive understanding of the complex challenges posed by AI implementation in healthcare settings. By categorizing and elucidating these genres, the study aims to facilitate the development of empirical qualitative and quantitative research, fostering evidence-based approaches to address AI-related risks in healthcare effectively. This endeavor contributes to building a robust knowledge base that can inform the formulation of risk mitigation strategies, ensuring safe and efficient integration of AI technologies in healthcare practices. Thus, it is important to study AI risks in healthcare to build better and efficient AI systems and mitigate risks

Econometrics Modelling Approach to Examine the Effect of STEM Policy Changes on Asian Students Enrollment Decision in USA

Academic research has shown significant interest in international student mobility, with previous literature primarily focusing on the migration industry from a political and public policy perspective. For many countries, international student mobility plays a crucial role in bolstering their economies through financial gains and attracting skilled immigrants. While previous studies have explored the determinants of mobility and country economic policies, only a few have examined the impact of policy changes on mobility trends. In this study, the researchers investigate the influence of immigration policy changes, particularly the optional practical training (OPT) extension on STEM programs, on Asian students' preference for enrolling in STEM majors at universities. The study utilizes observational data and employs a quasi-experimental design, analysing the information using the difference-in-difference technique. The findings of the research indicate that the implementation of the STEM extension policy in 2008 has a significant effect on Asian students' decisions to enroll in a STEM major. Additionally, the study highlights the noteworthy role of individual factors such as the specific STEM major, terminal degree pursued, and gender in influencing Asian students' enrollment decisions

Impact of Data Quality and Quantity on its Effectiveness on Multi-Stage Transfer Learning Using MRI Medical Images

Multi-Stage Transfer Learning (MSTL) has been becoming a very promising area of research in the field of medical imaging. Model architecture based on Multi-Stage TL provided promising results surpassing the previous standards. In our study, we provide an overview of Multi-Stage TL and its implementation in medical imaging followed by reviewing the research work in the field of transfer learning in medical imaging. Our objective is to investigate and understand the different effects of data quality and quantity on Multi-Stage Transfer learning using the MRI images. We propose an MSTL model comprises of 4 different stages, in the first stage the model adapts the features and weights from a pre-trained network, second stage will include the domain adaptation having similar domain data with previous weights being fine-tuned, third stage is split into 3 separate layers each investigating the impact of Data Quality, Quantity and image features. In the final stage, we will apply the weights learned from the previous stages into the completely new dataset (Target/Problem area) and analyze its effects. Our study discusses the utilization of Multi-Stage transfer in medical imaging using the CNN architectures such as Inception V-3, AlexNet, and ResNet and investigate the current challenges in medical imaging domain such as computational complexity, domain adaptation and effectiveness of data quality and quantity using TL and Multi-Stage TL and proposed the future research areas

Navigating the Docker Ecosystem: A Comprehensive Taxonomy and Survey

The cloud computing landscape is rapidly expanding and growing in complexity. It has witnessed the emergence of Cloud Computing as a widely adopted model for efficiently processing large volumes of data by harnessing clusters of commodity computers. This evolution enables the handling of massive data through on-demand services, relying on numerous microservices with diverse dependencies. The technology of containers ensures secure storage, allowing for large-scale data processing with high scalability and portability. Container technology, particularly exemplified by Docker in the last decade, plays a pivotal role in this scenario. It empowers microservices to process data swiftly, enabling developers to dynamically scale these services in real-time. This paper initiates by establishing a comprehensive taxonomy for delineating container architecture. Focusing specifically on Docker containers, we scrutinize various existing container-related literature. Through this taxonomy and survey, we not only discern similarities and disparities in the architectural approaches of Docker container technology but also pinpoint areas necessitating further research