Automatically Assessing Quality of Online Health Articles

Afsana, Fariha; Kabir, Muhammad Ashad; Hassan, Naeemul; Paul, Manoranjan

doi:10.1109/jbhi.2020.3032479

Cited by 12 publications

(16 citation statements)

References 37 publications

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“…The developed model can identify web pages that make unproven cancer treatment fully automatic and substantially better than the previous web-based tools and state-of-the-art search engine technologies. The study by [41] developed a supervised learning approach using the Support Vector Machine (SVMLight) toolkit. The approach was designed to predict the reliability of medical webpages automatically.…”

Section: Related Workmentioning

confidence: 99%

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering

Nyanjara¹,

Machuve²,

Nykänen³

2022

JDAIP

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High maternal and child deaths in developing countries are frequently linked to poor health services provided to pregnant women and children. To improve the quality of maternal, neonatal and child health (MNCH) services, the government and other stakeholders in MNCH emphasize the importance of quality assessment. However, effective quality assessment approaches are mostly lacking in most developing countries, particularly in Tanzania. This study, therefore, aimed at developing a quality assessment approach that can effectively assess and report on the quality of MNCH services. Due to the need for a good quality assessment approach that suits a resource-constrained environment, machine learning-based approach was proposed and developed. K-means algorithm was used to develop a clustering model that groups MNCH data and performs cluster summarization to discover the knowledge portrayed in each group on the quality of MNCH services. Results confirmed the clustering model's ability to assign the data points into appropriate clusters; cluster analysis with the collaboration of MNCH experts successfully discovered insights on the quality of services portrayed by each group.

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Section: Related Workmentioning

confidence: 99%

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering

Nyanjara¹,

Machuve²,

Nykänen³

2022

JDAIP

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“…With respect the subject of study, research shows the great growth of internet searches on health issues, being this way that [11] Jozsef in its research on Health Information on the Internet, tells us that research has revealed that more and more Internet users are accessing health-related websites to search for health information and are relying on it to answer their health questions. Therefore, in the following study [12] it is confirmed that Internet searches can help people find not only the symptoms of the disease, but also various treatment options, this information is largely hosted in social media communities such as blogs, social networks, e-mails, among others [13].…”

Section: Literature Reviewmentioning

confidence: 79%

Implementation of an Expert System for Automated Symptom Consultation in Peru

Torres¹,

Aponte²,

Andrade–Arenas³

2021

IJACSA

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The human being has a fragile life, and is attacked by different diseases throughout his life, neglecting or ignoring some of them because it is considered minimal, can be fatal, but many do not want to attend a health center, so they seek your symptoms on the Internet and finding pages with false information, that is the problem that we will address in this investigation. The objective of the research is to implement an expert system, creating a web page that provides real information when a user enters their symptoms. This was achieved based on the logic of rules developed in Prolog, so when a user fills out the created questionnaire, the expert system will follow. the rules to conclude with the desired diagnosis; all these steps were carried out using the buchanan methodology. The result was an improvement in the accessibility of truthful information through the Internet, facilitating the management of appointments of users if they have a serious illness, or the treatment in case of a minor illness. The beneficiaries of the research were the population that required the use of the automated query application.

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“…Some studies have deployed different feature selection techniques and algorithms and benchmarked the performance using different ML models. [10][11][12] Various previous studies have applied ML and DL techniques to build prediction models for the credibility assessment of web content. Recently, researchers have considered credibility assessment a text classification problem and used different DL techniques to capture credibility features from different perspectives for evaluation.…”

Section: Introductionmentioning

confidence: 99%

An attention‐based deep learning model for credibility assessment of online health information

Padhy

Rathore

2023

Computational Intelligence

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With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.

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Automatically Assessing Quality of Online Health Articles

Cited by 12 publications

References 37 publications

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering

Implementation of an Expert System for Automated Symptom Consultation in Peru

An attention‐based deep learning model for credibility assessment of online health information

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Automatically Assessing Quality of Online Health Articles

Cited by 12 publications

References 37 publications

Maternal and Child Health Care Quality Assessment: An Improved Approach Using &lt;i&gt;K&lt;/i&gt;-Means Clustering

Maternal and Child Health Care Quality Assessment: An Improved Approach Using &lt;i&gt;K&lt;/i&gt;-Means Clustering

Implementation of an Expert System for Automated Symptom Consultation in Peru

An attention‐based deep learning model for credibility assessment of online health information

Contact Info

Product

Resources

About

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering

Maternal and Child Health Care Quality Assessment: An Improved Approach Using <i>K</i>-Means Clustering