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- Keyword
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- COVID-19 (2)
- Data analytics (2)
- Advertising (1)
- Anxiety (1)
- Behavioral change (1)
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- Chronic disease management (1)
- Effectiveness of campaigns (1)
- Facebook advertising (1)
- Graph auto-encoder (1)
- Graph neural network (1)
- Health information (1)
- Healthcare informedness (1)
- Healthcare organizations (1)
- Human cancers (1)
- Knowledge (1)
- Misinformation (1)
- Multi-view (1)
- NLP (1)
- Physicians (1)
- Precision public health (1)
- Public health (1)
- Public health campaigns (1)
- Social media (1)
- Social media advertising (1)
- Synthetic lethality (1)
- Tailored health communication (1)
- Theory of informedness (1)
- Transtheoretical model (1)
- Work-from-home (1)
Articles 1 - 6 of 6
Full-Text Articles in Databases and Information Systems
Channel Integration Services In Online Healthcare Communities, Anqi Zhao, Qian Tang
Channel Integration Services In Online Healthcare Communities, Anqi Zhao, Qian Tang
Research Collection School Of Computing and Information Systems
In online healthcare communities, channel integration services have become the bridge between online and offline channels, enabling patients to easily migrate across channels. Different from pure online services, online-to-offline (On2Off) and offline-to-online (Off2On) channel integration services involve both channels. This study examines the interrelationships between pure online services and channel integration services. Using a panel dataset composed of data from an online healthcare community, we find that pure online services decrease patients’ demand for On2Off integration services but increase their use of Off2On integration services. Our findings suggest that providing healthcare services online can reduce online patients’ needs to visit …
Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li
Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li
Research Collection School Of Computing and Information Systems
Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We …
Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber
Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber
Research Collection School Of Computing and Information Systems
Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media–targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience …
Knowledge And Anxiety About Covid-19 In The State Of Qatar, And The Middle East And North Africa Region—A Cross Sectional Study, Sathyanarayanan Doraiswamy, Sohaila Cheema, Maisonneuve Patrick, Amit Abraham, Ingmar Weber, Jisun An, Albert B. Lowenfels, Ravinder Mamtani
Knowledge And Anxiety About Covid-19 In The State Of Qatar, And The Middle East And North Africa Region—A Cross Sectional Study, Sathyanarayanan Doraiswamy, Sohaila Cheema, Maisonneuve Patrick, Amit Abraham, Ingmar Weber, Jisun An, Albert B. Lowenfels, Ravinder Mamtani
Research Collection School Of Computing and Information Systems
While the coronavirus disease 2019 (COVID-19) pandemic wreaked havoc across the globe, we have witnessed substantial mis- and disinformation regarding various aspects of the disease. We conducted a cross-sectional study using a self-administered questionnaire for the general public (recruited via social media) and healthcare workers (recruited via email) from the State of Qatar, and the Middle East and North Africa region to understand the knowledge of and anxiety levels around COVID-19 (April–June 2020) during the early stage of the pandemic. The final dataset used for the analysis comprised of 1658 questionnaires (53.0% of 3129 received questionnaires; 1337 [80.6%] from the …
Enhancing Healthcare Professional And Caregiving Staff Informedness With Data Analytics For Chronic Disease Management, Na Liu, Robert John Kauffman
Enhancing Healthcare Professional And Caregiving Staff Informedness With Data Analytics For Chronic Disease Management, Na Liu, Robert John Kauffman
Research Collection School Of Computing and Information Systems
An important area in healthcare to which data analytics can be applied is chronic disease management. The chronic care model is mostly patient-centric, so patients have been considered as the end users of data analytics. The information needs of healthcare providers have been overlooked. Drawing upon the theory of informedness and the transtheoretical model of health behavior change, we use a multicase study approach to investigate the information needs of different caregiving stakeholders in the spectrum of chronic diseases, and how data analytics can be designed to meet the varying needs of professionals and staff to support their informedness.
Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam
Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam
Research Collection School Of Computing and Information Systems
The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …