scholarly journals Evaluation of an international medical E-learning course with natural language processing and machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aditya Borakati
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Natural Language Processing ◽  
Medical Students ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Small Scale ◽  
E Learning ◽  
The Impact

Abstract Background In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods. Method This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7%) were medical students; 1067 (66.2%) entered feedback. 1031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity. Conclusions E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

scholarly journals Evaluation of an International Medical E-Learning Course with Natural Language Processing and Machine Learning

2020 ◽  
Author(s):  
Aditya Borakati
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Natural Language Processing ◽  
Medical Students ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Small Scale ◽  
E Learning ◽  
The Impact

Abstract Background: In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods.Method: This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results: 1,611 collaborators from 24 countries completed the e-learning course; 1,396 (86.7%) were medical students; 1,067 (66.2%) entered feedback. 1,031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was +1.54/5 (5: most positive; SD 1.19) and +0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity.Conclusions: E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

scholarly journals Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop)

2020 ◽  
Vol 4 (1) ◽  
pp. 208
Author(s):  
Albert Yakobus Chandra ◽  
Didik Kurniawan ◽  
Rahmat Musa
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Services ◽  
Human Language ◽  
Coffee Shop ◽  
The Impact

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers

scholarly journals Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

2017 ◽  
Vol 25 (4) ◽  
pp. 1170-1187 ◽  
Author(s):  
Madhav Erraguntla ◽  
Josef Zapletal ◽  
Mark Lawley
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Support Vector ◽  
Human Populations ◽  
Illustrative Case ◽  
The Impact ◽  
Disease Analysis

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

scholarly journals A Text Mining using Web Scraping for Meaningful Insights

2021 ◽  
Vol 2089 (1) ◽  
pp. 012048
Author(s):  
Kishor Kumar Reddy C ◽  
P R Anisha ◽  
Nhu Gia Nguyen ◽  
G Sreelatha
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Learning Technology ◽  
Web Scraping ◽  
Textual Data ◽  
The Web

Abstract This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.

scholarly journals Information retrieval with text mining for Decision Support System

2012 ◽  
Vol 24 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Mahmuda Rahman
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Decision Support ◽  
Text Mining ◽  
Decision Support System ◽  
Natural Language ◽  
Key Words ◽  
Language Processing ◽  
Support System

Key words: Natural language processing; C4.5 classification; DSS, machine learning; KNN clustering; SVMDOI: http://dx.doi.org/10.3329/bjsr.v24i2.10768 Bangladesh J. Sci. Res. 24(2):117-126, 2011 (December) 

scholarly journals Discovering future of the social trends using social media tools

2017 ◽  
Vol 7 (3) ◽  
pp. 153-159
Author(s):  
Omer Sevinc ◽  
Iman Askerbeyli ◽  
Serdar Mehmet Guzel
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Web Mining ◽  
Media Analysis ◽  
Social Web ◽  
Social Media Tools

Social media has been widely used in our daily lives, which, in essence, can be considered as a magic box, providing great insights about world trend topics. It is a fact that inferences gained from social media platforms such as Twitter, Faceboook or etc. can be employed in a variety of different fields. Computer science technologies involving data mining, natural language processing (NLP), text mining and machine learning are recently utilized for social media analysis. A comprehensive analysis of social web can discover the trends of the public on any field. For instance, it may help to understand political tendencies, cultural or global believes etc. Twitter is one of the most dominant and popular social media tools, which also provides huge amount of data. Accordingly, this study proposes a new methodology, employing Twitter data, to infer some meaningful information to remarks prominent trend topics successfully. Experimental results verify the feasibility of the proposed approach. Keywords: Social web mining, Tweeter analysis, machine learning, text mining, natural language processing.  

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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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