scholarly journals Ambient virtual scribes: Mutuo Health’s AutoScribe as a case study of artificial intelligence-based technology

2019 ◽  
Vol 33 (1) ◽  
pp. 34-38 ◽  
Author(s):  
Noah H. Crampton
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Medical Record ◽  
Language Processing ◽  
Poor Quality ◽  
Health Data ◽  
Free Text ◽  
High Quality ◽  
Data Analytic

Studies show that clinicians are increasingly burning out in large part from the clerical burden associated with using Electronic Medical Record (EMR) systems. At the same time, recently developed health data analytic algorithms struggle with poor quality free-text entered data in these systems. We developed AutoScribe using artificial intelligence–based natural language processing tools to automate these clerical tasks and to output high-quality EMR data. In this article, we describe the benefits and drawbacks of our technology. Furthermore, we describe how we are positioning our company’s culture within the existing healthcare system and suggest steps leaders of the system should consider in order to ensure that potentially transformative artificial intelligence–based technologies like ours are optimally adopted.

scholarly journals Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2300
Author(s):  
Rade Matic ◽  
Milos Kabiljo ◽  
Miodrag Zivkovic ◽  
Milan Cabarkapa
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Communication Channels ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Customer Services ◽  
The Right

In recent years, gradual improvements in communication and connectivity technologies have enabled new technical possibilities for the adoption of chatbots across diverse sectors such as customer services, trade, and marketing. The chatbot is a platform that uses natural language processing, a subset of artificial intelligence, to find the right answer to all users’ questions and solve their problems. Advanced chatbot architecture that is extensible, scalable, and supports different services for natural language understanding (NLU) and communication channels for interactions of users has been proposed. The paper describes overall chatbot architecture and provides corresponding metamodels as well as rules for mapping between the proposed and two commonly used NLU metamodels. The proposed architecture could be easily extended with new NLU services and communication channels. Finally, two implementations of the proposed chatbot architecture are briefly demonstrated in the case study of “ADA” and “COVID-19 Info Serbia”.

scholarly journals Using sentiment analysis to predict opinion inversion in Tweets of political communication

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yogev Matalon ◽  
Ofir Magdaci ◽  
Adam Almozlino ◽  
Dan Yamin
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Natural Language Processing ◽  
Random Forest ◽  
Political Communication ◽  
Language Processing ◽  
Source Text ◽  
Social Media Networks ◽  
Contrasting View

AbstractSocial media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.

scholarly journals Changing Paradigm of Consumer Experience Through Martech – A Case Study on Indian Online Retail Industry

2021 ◽  
pp. 186-199
Author(s):  
Suresh Raghavan ◽  
Ramesh Pai
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
Natural Language Processing ◽  
Internet Of Things ◽  
Language Processing ◽  
Secondary Data ◽  
Retail Industry ◽  
Online Retail ◽  
Marketing Technology

Purpose: Retail selling is an inevitable economic activity in any country’s economy. In India, the retail industry contributes 10% of its GDP. The invention of the Internet and technological advancement in digital marketing has helped the online retail industry to grow exponentially. Digital advancement has created an environment where customers are more informed, hence chooser and demanding. Marketing technology or ‘Martech’ encompasses technology to reach online customers to provide pre-eminent customer experience, to meaningfully engage and retain them. The latest such technologies are Artificial Intelligence, Augmented/Virtual Reality, Internet of Things, Natural Language Processing, Block Chain Technology, etc. This paper is an exploratory study using secondary data, which revealed that many firms have already adopted and most others are willing to adopt such advanced technologies in the near future as they are convinced that such technologies can phenomenally change the marketing strategies. This study also explores the current status of retail industry and also the opportunities and challenges of adopting these technologies from marketers’ perspective. It also proposes suggestions from a user perspective, based on the analysis findings. Design Methodology: Developing a conceptual framework using primary and secondary data collected from various studies published by the govt, global research firms, blogs, and other internet articles. Findings: This paper revealed a dire need to adapt the marketing technology in the retailing industry for future survival. The analysis also revealed that the affinity for internet and online purchases is increasing exponentially hence the latest technology such as Artificial Intelligence, Internet of Things, Machine Learning, Augmented Reality/Virtual Reality, and Natural Language Processing are tremendously revolutionizing the customer experience and thereby increased level of Customer Engagement. Type of Paper: Case study-based Research Analysis.

scholarly journals Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese

2021 ◽  
Vol 11 (22) ◽  
pp. 11018
Author(s):  
Xianwen Liao ◽  
Yongzhong Huang ◽  
Changfu Wei ◽  
Chenhao Zhang ◽  
Yongqing Deng ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Low Frequency ◽  
Word Embeddings ◽  
High Quality ◽  
Representation Model ◽  
Explanatory Note ◽  
Efficient Estimate

Obtaining high-quality embeddings of out-of-vocabularies (OOVs) and low-frequency words is a challenge in natural language processing (NLP). To efficiently estimate the embeddings of OOVs and low-frequency words, we propose a new method that uses the dictionary to estimate the embeddings of OOVs and low-frequency words. More specifically, the explanatory note of an entry in dictionaries accurately describes the semantics of the corresponding word. Naturally, we adopt the sentence representation model to extract the semantics of the explanatory note and regard the semantics as the embedding of the corresponding word. We design a new sentence representation model to encode sentences to extract the semantics from the explanatory notes of entries more efficiently. Based on the assumption that the higher quality of word embeddings will lead to better performance, we design an extrinsic experiment to evaluate the quality of low-frequency words’ embeddings. The experimental results show that the embeddings of low-frequency words estimated by our proposed method have higher quality. In addition, both intrinsic and extrinsic experiments show that our proposed sentence representation model can represent the semantics of sentences well.

Artificial intelligence driven framework for the structurization of free-text diagnostic reports

2020 ◽  
Author(s):  
◽  
Pericles Stavros Giannaris
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Free Text ◽  
Stream Data ◽  
Stream Data Mining ◽  
Starting Point

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Free-text sections of diagnostic reports contain a wealth of data on patients, diseases, and complex diagnostic processes. However, free-text data are a poor starting point for computer-based analytics. The majority of natural language processing (NLP) based approaches lack a capacity to accurately extract complex diagnostic entities and their relationships as well as to provide adequate knowledge representation (KR) for down-stream data mining applications. In order to overcome these limitations, a novel informatics framework is introduced for the analysis of free-text diagnostic reports. The framework is based on artificial intelligence (AI) modeling. Here, AI-based modeling integrates natural language processing information extraction techniques (NLP-IE), ontology-based knowledge representation, n-ary relations according to ontological patterns, and information entropy-based data mining approaches. Diagnostic reports are transformed to knowledge graphs (KGs) of relational triples for further analysis using computers. The goal is to facilitate analysis of diagnostic reports using computers. This informatics framework has potential to broadly impact diagnostic medicine and to be extended to other biomedical domains as well.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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