An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory Technology

Technology trend analysis uses data relevant to historical performance and extrapolates it to estimate and assess the future potential of technology. Such analysis is used to analyze emerging technologies or predict the growing markets that influence the resulting social or economic development to assist in effective decision-making. Traditional trend analysis methods are time-consuming and require considerable labor. Moreover, the implemented processes may largely rely on the specific knowledge of the domain experts. With the advancement in the areas of science and technology, emerging cross-domain trends have received growing attention for its considerable influence on society and the economy. Consequently, emerging cross-domain predictions that combine or complement various technologies or integrate with diverse disciplines may be more critical than other tools and applications in the same domain. This study uses a design science research methodology, a text mining technique, and social network analysis (SNA) to analyze the development trends concerning the presentation of the product or service information on a company's website. This study applies regulatory technology (RegTech) as a case to analyze and justify the emerging cross-disciplinary trend. Furthermore, an experimental study is conducted using the Google search engine to verify and validate the proposed research mechanism at the end of this study. The study results reveal that, compared with Google Trends and Google Correlate, the research mechanism proposed in this study is more illustrative, feasible, and promising because it reduces noise and avoids the additional time and effort required to perform a further in-depth exploration to obtain the information.

CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS (i.e., the capability of understanding the sequential semantics in important query words), while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41K repositories. Experimental results showed that CodeMatcher achieves an MRR (a widely used accuracy measure for code search) of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46%, respectively. Our proposed model is over 1.2K times faster than DeepCS. Moreover, CodeMatcher outperforms two existing online search engines (GitHub and Google search) by 46% and 33%, respectively, in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.

Quality-Based Evaluation of Patient-Facing Online Education Materials Regarding Facial Filler Procedures

Injectable facial fillers have become tremendously more popular in recent years, and the Internet offers a proportional amount of consumer-facing educational material. This study sought to explore the quality of these online materials. The top 20 Web sites offering educational materials about facial filler were identified via Google search and sorted by source: Medical Professional Boards, Hospitals and Providers, Medical News and Reference, and Fashion. The materials were assessed for overall quality with the validated DISCERN instrument. The authors also assessed understandability and actionability (Patient Education Material Assessment Tool - PEMAT), accuracy, comprehensiveness, and readability (Flesch-Kincaid Grade Level and Flesch Reading Ease). The mean DISCERN score was 46.9 ± 7.6, which is considered “fair” quality educational material; above “poor,” but below “good” and “excellent.” Understandability and actionability scores were low, particularly with respect to visual aids. The materials were generally accurate (76–99%), but scored poorly in comprehensiveness, as 15% failed to mention any risks/adverse effects and only 35% mentioned cost. On average, readability was at an 11th grade level, far more complex than ideal (< 6th grade level). Information disseminated from seemingly reputable sources such as professional boards and hospitals/providers were not of higher quality or superior in any of the above studied domains. In conclusion, online educational materials related to injectable facial fillers are of subpar quality, including those from academic and professional organizations. Visual aids were particularly weak. The facial rejuvenation community should make a concerted effort to set a higher standard for disseminating such information.

State of the Research on Artificial Intelligence Based Apps for Post-Secondary Students with Disabilities

We conducted a general Google search and a scoping review of various types of artificial intelligence (AI) based technology – mobile, web-based, software, hardware – used by college and university students to do schoolwork. The main findings indicate that (1) there is no generally agreed upon definition of AI, and (2) there is a huge discrepancy between the popular press articles that are behind the AI hype and the scientific literature. The popular press provides an overview of the AI tools available to students with disabilities and discusses how students can use these tools. The scientific literature is primarily devoted to tool development and has poor methodology. We conclude that the potential of AI for post-secondary students with disabilities is enormous, but that informed research about these tools is scant, with a profound lack of demonstrated scalability. Research needs to address “real-world” uses of AI-based tools by post-secondary students with disabilities.

Open-access database of literature derived drug-related Torsade de Pointes cases

Since an introduction of an ICH guidance in 2005, no new drugs were withdrawn from the market because of the causation of Torsade de Pointes (TdP). However, the risk of TdP is still a concern for marketed drugs. TdP is a type of polymorphic ventricular tachycardia which may lead to sudden cardiac death. QT/QTc interval prolongation is considered a sensitive, but not specific biomarker. To improve the effectiveness of studies’ workflow related to TdP risk prediction we created an extensive, structured, open-access database of drug-related TdP cases. PubMed, Google Scholar bibliographic databases, and the Internet, via the Google search engine, were searched to identify eligible reports. A total of 424 papers with a description of 634 case reports and observational studies were included. Each paper was manually examined and listed with up to 53 variables related to patient/population characteristics, general health parameters, used drugs, laboratory measurements, ECG results, clinical management, and its outcomes, as well as suspected drug’s properties and its FDA adverse reaction reports. The presented database may be considered as an extension of the recently developed and published database of drug cardiac safety-related information, part of the tox-portal project providing resources for cardiac toxicity assessment.

What experts say about COVID-19 vaccine work in human body

This health paper analysis discusses what experts think about the work of the COVID-19 vaccine in the human body. This study is part of general public health literacy. To facilitate the discussion, we obtained data through a Google search engine on many well-known publications concerned with health issues, especially the coronavirus prevention vaccination program. The publication journals we mean are Medpub, Google Book, Esavier, Sagepub, Academic research, Taylor and France, and several other publications. We managed this paper in a qualitative design for secondary data exploration. Meanwhile, our research efforts are carried out. Namely, we use data coding, evaluation, and in-depth interpretation to draw conclusions that can answer the questions of this study validly and reliably. The result is that vaccine programs function by training the immune system to detect and fight viruses and bacteria. Do this; pathogenic molecules must be delivered into the body to elicit an immune response. These molecules, known as antigens, can be found in all viruses and bacteria.

Healthcare financing in Egypt: a systematic literature review.

Background The Egyptian healthcare system has multiple stakeholders, including a wide range of public and private healthcare providers and several financing agents. This study sheds light on the healthcare system’s financing mechanisms and the flow of funds in Egypt. It also explores the expected challenges facing the system with the upcoming changes. Methods We conducted a systematic review of relevant papers through the PubMed and Scopus search engines, in addition to searching gray literature through the ISPOR presentations database and the Google search engine. Articles related to Egypt’s healthcare system financing from 2009 to 2019 were chosen for full-text review. Data were aggregated to estimate budgets and financing routes. Results We analyzed the data of 56 out of 454 identified records. Governmental health expenditure represented approximately one-third of the total health expenditure (THE). Total health expenditure as a percent of gross domestic product (GDP) was almost stagnant in the last 12 years, with a median of 5.5%. The primary healthcare financing source is out-of-pocket (OOP) expenditure, representing more than 60% of THE, followed by government spending through the Ministry of Finance, around 37% of THE. The pharmaceutical expenditure as a percent of THE ranged from 26.0 to 37.0%. Conclusions Although THE as an absolute number is increasing, total health expenditure as a percentage of GDP is declining. The Egyptian healthcare market is based mainly on OOP expenditures and the next period anticipates a shift toward more public spending after Universal Health Insurance gets implemented.

Critical Quality and Readability Analysis of Online Patient Education Materials on Parotidectomy: A Cross-Sectional Study

Purpose: Complications related to parotidectomy can cause significant morbidity, and thus, the decision to pursue this surgery needs to be well-informed. Given that information available online plays a critical role in patient education, this study aimed to evaluate the readability and quality of online patient education materials (PEMs) regarding parotidectomy. Methods: A Google search was performed using the term “parotidectomy” and the first 10 pages of the search were analyzed. Quality and reliability of the online information was assessed using the DISCERN instrument. Flesch-Kincaid Grade Level (FKGL) and Flesch-Reading Ease Score (FRE) were used to evaluate readability. Results: Thirty-five PEMs met the inclusion criteria. The average FRE score was 59.3 and 16 (46%) of the online PEMs had FRE scores below 60 indicating that they were fairly difficult to very difficult to read. The average grade level of the PEMs was above the eighth grade when evaluated with the FKGL. The average DISCERN score was 41.7, which is indicative of fair quality. There were no significant differences between PEMs originating from medical institutions and PEMs originating from other sources in terms of quality or readability. Conclusion: Online PEMs on parotidectomy may not be comprehensible to the average individual. This study highlights the need for the development of more appropriate PEMs to inform patients about parotidectomy.

The Knowledge Economy and Social Impact

There is not a one-size-fits-all definition of “social impact.” In fact, in a Google search for “What is social impact?” more than 400 results appear. This chapter will highlight global initiatives led by OneSight, an NGO that is utilizing new technologies to combat the vision care crisis, and CanopyLAB, a software company that has teamed up with over 120 NGOs around the world to create and provide online courses utilizing artificial intelligence.

Why-type Question to Query Reformulation for efficient Document Retrieval

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.