scholarly journals IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1469 ◽  
Author(s):  
Pradeepa Sampath ◽  
Gayathiri Packiriswamy ◽  
Nishmitha Pradeep Kumar ◽  
Vimal Shanmuganathan ◽  
Oh-Young Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Vital Role ◽  
Online Health Communities ◽  
Health Community ◽  
Chi Squared ◽  
Health Related ◽  
Health Communities ◽  
Processing Techniques

The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.

scholarly journals Development and Evaluation of the Canteen Connect Online Health Community: Using a Participatory Design Approach in Meeting the Needs of Young People Impacted by Cancer

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 50
Author(s):  
Jennifer Cohen ◽  
Pandora Patterson ◽  
Melissa Noke ◽  
Kristina Clarke ◽  
Olga Husson
Keyword(s):  
Participatory Design ◽  
Positive Impact ◽  
Familial Cancer ◽  
Online Health Communities ◽  
Implementation Evaluation ◽  
Online Health Community ◽  
Health Community ◽  
Health Related ◽  
Adolescent And Young Adults ◽  
Health Communities

Adolescent and young adults (AYAs) impacted by their own or familial cancer require information and peer support throughout the cancer journey to ameliorate feelings of isolation. Online Health Communities (OHC) provide social networks, support, and health-related content to people united by a shared health experience. Using a participatory design (PD) process, Canteen developed Canteen Connect (CC), an OHC for AYAs impacted by cancer. This manuscript outlines the process used to develop CC: (1) A mixed-methods implementation evaluation of Version I of CC (CCv.1); (2) Qualitative workshops utilizing strengths-based approaches of PD and appreciative inquiry to inform the development of CC Version 2 (CCv.2); quantitative implementation evaluation to assess the appropriateness, acceptability, and effectiveness of CCv.2. Through several iterations designed and tested in collaboration with AYAs, CCv.2 had improvements in the user experience, such as the ability to send a private message to other users and the site becoming mobile responsive. Results from the evaluation showed CCv.2 was appropriate for connecting with other AYAs. Most AYAs reported satisfaction with CCv.2 and a positive impact on their feelings of sadness, worry, and/or anxiety. CCv.2 fills an important service provision gap in providing an appropriate and acceptable OHC for AYAs impacted by cancer, with initial promising psychological outcomes.

scholarly journals Medical Applications using Blockchain and Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3928-3932
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Blockchain Technology ◽  
Improving Accuracy ◽  
Processing Techniques ◽  
Cryptographic Techniques

Blockchain was particularly used in Cryptocurrency technologies. Prior to 20th century there was no other technologies for determining the health of a person naturally. At the dawn of the 21st Century machine learning played a vital role in determining the health of a person using various algorithms and natural language processing techniques. Now for every machine learning technique to work for it needs data. Data is very important as far as providing information is concerned. Data sharing plays a vital role in improving accuracy of techniques involved. Along the blockchain technology plays a vital role in this aspect. Thus, the merging of these two techniques involve provides highly accurate results in terms of machine learning with privacy and reliability of Blockchain technology. This technique uses natural language processing techniques which focuses basically mainly on healthcare techniques such as cancer detection, prediction of machines used in healthcare etc. Prior to healthcare which is used in blockchain it was used in cryptographic techniques only. Also, this technology can be used to provide medical suggestions to the doctors based on the condition of the patient. The accuracy of this method can be increased more using providing as much data as we can. This combination of Blockchain and machine learning algorithms can be used widely in healthcare, where the data is highly secured and there is no fear of data loss. This paper involves how combining these two technologies can be helpful in healthcare.

Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms

2020 ◽  
pp. 1-9
Author(s):  
P. Ajitha ◽  
A. Sivasangari ◽  
R. Immanuel Rajkumar ◽  
S. Poonguzhali
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Sentiment Mining ◽  
Processing Techniques ◽  
Text Sentiment Analysis ◽  
Insight Into

Text Sentiment Analysis is a system where text feeling polarity is positive or negative or neutral from a series of texts or documents or public opinions on a particular product or general subject. Using machine learning and natural language processing techniques, the current work aims to gain insight into sentiment mining on tweets. Text classification is accomplished using Machine Learning Algorithm-based fusion technique. This research suggested a system for grading feelings based on a lexicon. Bag-of-words (BOW) or lexicon-based methodology is currently the main standard way of modeling text for machine learning in sentiment analysis approaches. Marketers can use sentiment analysis to analyze their business and services, public opinion, or to evaluate customer satisfaction. Organizations can even use this analysis to gather significant feedback on issues related to newly released products. The main objective of this is to resolve the data overload problem.

Exploring the collective knowledge curation process of online health communities

2016 ◽  
Author(s):  
◽  
Wanli Xing
Keyword(s):  
Social Support ◽  
Information Flow ◽  
Flow Patterns ◽  
University Of Missouri ◽  
Online Health Communities ◽  
Health Community ◽  
Health Related ◽  
Health Communities ◽  
The University ◽  
Relationship Of

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] More and more people turn to online health communities for social support to satisfy their health-related needs. Previous studies on social support and online health communities in general have focused on the content of social support and the relationship of social support with other entities using traditional social science methods. Little is known about how social support facilitate the knowledge curation process in an online health community. Moreover, the presence of misinformation in online health communities also calls for research into the knowledge curation process in order to reduce the risk of misinformation. This study uses data mining technologies to analyze around one million posts across 23 online health communities. It aims to reveal how information, through social support, flows between the community users working as a whole to dynamically curate knowledge and further interacts with information accuracy. This data-centric research in online health communities 1) discovered that xperiphery users instead of core users dominate the quantitative and content information flow; 2) identified three temporal information flow patterns for the knowledge curation process -- each with distinct characteristics; 3) found that information accuracy differed significantly over the identified information flow patterns and time and the information accuracy variation trends with each information flow pattern was identified as well. These findings not only have important implications for social support use, delivery and social support research methodologies but also can inform future online health platform design.

scholarly journals Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

2019 ◽  
Vol 28 (01) ◽  
pp. 208-217 ◽  
Author(s):  
Mike Conway ◽  
Mengke Hu ◽  
Wendy W. Chapman
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online Health Communities ◽  
Machine Learning Methods ◽  
Health Communities ◽  
Health Applications

Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

Examining online health community users’ sharing behaviour: A social influence perspective

2021 ◽  
pp. 026666692110071
Author(s):  
Tao Zhou
Keyword(s):  
Social Influence ◽  
Influence Factors ◽  
Privacy Concern ◽  
Social Influence Theory ◽  
Online Health Communities ◽  
Related Information ◽  
Health Community ◽  
The Social ◽  
Health Related ◽  
Health Communities

Online health communities (OHC) provide a platform for users to exchange health-related information and seek emotional support. However, users often lack the intention to share their knowledge, which may lead to the failure of OHC. Drawing on the social influence theory, this research examined OHC users’ sharing behaviour. The results indicated that users’ sharing intention is influenced by three social influence factors, which include subjective norm, social identity and group norm. In addition, social support and privacy concern have effects on these three social influence factors. The results imply that OHC need to leverage social influence in order to facilitate users’ sharing behaviour.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

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