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Data Mining and Sentiment Analysis in Healthcare
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Data Mining and Sentiment Analysis in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 21027

Special Issue Editors

Dr. Adnan Muhammad Shah

E-Mail Website
Guest Editor
Chair of Marketing and Innovation, University of Hamburg, Hamburg, Germany
Interests: machine learning; text mining and sentiment analysis; big data and business analytics; healthcare analytics; healthcare IT and data mining
Dr. Wazir Muhammad

E-Mail Website
Guest Editor
Department of Physics, Florida Atlantic University, Boca Raton, FL, USA
Interests: data mining; big data analytics; machine learning; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In the present digital era, sentiment analysis or opinion mining in the healthcare domain is becoming increasingly popular as users' feedback are analyzed from social media, blogs, websites, and clinical text. Sentiment analysis incorporates several fields, including natural language processing, text mining, information retrieval, computational linguistics and data mining. Sentiment analysis is widely used in politics, marketing, and tourism. It is also useful in healthcare, including determining patients' moods, emotional tone (positive or negative), syndromes, and monitoring online conversations, etc. Thus, sentiment analysis and data mining in healthcare vulnerability may help to guide whether the individual’s opinion concerning a specific theme is negative, positive, or neutral, along with its level. The healthcare domain is going through significant modifications in its current practices and rules. Healthcare service providers can improve their patients' experiences by using sentiment analysis, one of the growing trends in healthcare. Data mining and machine learning algorithms are also discussed in the context of sentiment analysis in healthcare. There is a wide range of applications for sentiment analysis, but healthcare is one of the most prominent uses of technology advances in healthcare. Significant progress has been made in the healthcare domain, specifically in data mining and sentiment analysis. This issue's scope will cover unpublished and novel research in healthcare data mining and sentiment analysis.

This Special Issue aims to shed light on the sentiment analysis system for data analysis uses natural language processing (NLP) and machine learning techniques to offer weighted sentiment evaluations to themes, topics, and categories inside a document.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Sentiment/opinion/emotion analysis
  • Online health communities
  • Machine learning tools in healthcare
  • Real-time data analysis
  • Prompt and affective analysis of patient feedback
  • Healthcare analytics design and adoption
  • Affective analysis in healthcare texts
  • Big data analytics in healthcare
  • Social media analytics
  • Healthcare IT and data mining
  • Healthcare information retrieval and extraction

Dr. Adnan Muhammad Shah
Dr. Wazir Muhammad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data mining
  • sentiment analysis
  • affective analysis
  • big data
  • natural language processing
  • text mining
  • healthcare informatics
  • electronic clinical record
  • deep learning in healthcare
  • information retrieval

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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23 pages, 3004 KiB  
Article
Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed
by Zain Shaukat, Wisal Zafar, Waqas Ahmad, Ihtisham Ul Haq, Ghassan Husnain, Mosleh Hmoud Al-Adhaileh, Yazeed Yasin Ghadi and Abdulmohsen Algarni
Healthcare 2023, 11(21), 2864; https://doi.org/10.3390/healthcare11212864 - 31 Oct 2023
Cited by 1 | Viewed by 1978
Abstract
The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, [...] Read more.
The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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20 pages, 877 KiB  
Article
Multiple-Perspective Data-Driven Analysis of Online Health Communities
by Rana Alnashwan, Adrian O’Riordan and Humphrey Sorensen
Healthcare 2023, 11(20), 2723; https://doi.org/10.3390/healthcare11202723 - 12 Oct 2023
Cited by 2 | Viewed by 1170
Abstract
The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of [...] Read more.
The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of information—and the consequent ‘noise’ associated with large data volumes—can create difficulties for users. In this paper, we present a data-driven approach to better understand these data from multiple stakeholder perspectives. We utilise three techniques—sentiment analysis, content analysis, and topic analysis—to analyse user-generated medical content related to Lyme disease. We use a supervised feature-based model to identify sentiments, content analysis to identify concepts that predominate, and latent Dirichlet allocation strategy as an unsupervised generative model to identify topics represented in the discourse. We validate that applying three different analytic methods highlights differing aspects of the information different stakeholders will be interested in based on the goals of different stakeholders, expert opinion, and comparison with patient information leaflets. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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19 pages, 2771 KiB  
Article
A Study on Online Health Community Users’ Information Demands Based on the BERT-LDA Model
by Minhao Xiang, Dongdong Zhong, Minghua Han and Kun Lv
Healthcare 2023, 11(15), 2142; https://doi.org/10.3390/healthcare11152142 - 27 Jul 2023
Cited by 1 | Viewed by 1872
Abstract
As the economy and society develop and the standard of living improves, people’s health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories [...] Read more.
As the economy and society develop and the standard of living improves, people’s health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT’s semantic comprehension with LDA’s topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users’ emotional reactions and presents valuable insights for delivering personalized health information in online communities. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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28 pages, 6644 KiB  
Article
Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data
by Muhammad Ayaz, Muhammad Fermi Pasha, Tahani Jaser Alahmadi, Nik Nailah Binti Abdullah and Hend Khalid Alkahtani
Healthcare 2023, 11(12), 1729; https://doi.org/10.3390/healthcare11121729 - 13 Jun 2023
Cited by 2 | Viewed by 3197
Abstract
In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the [...] Read more.
In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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Review

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21 pages, 2154 KiB  
Review
Ensemble Learning for Disease Prediction: A Review
by Palak Mahajan, Shahadat Uddin, Farshid Hajati and Mohammad Ali Moni
Healthcare 2023, 11(12), 1808; https://doi.org/10.3390/healthcare11121808 - 20 Jun 2023
Cited by 39 | Viewed by 7018
Abstract
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches [...] Read more.
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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26 pages, 2766 KiB  
Review
A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain
by Pir Noman Ahmad, Adnan Muhammad Shah and KangYoon Lee
Healthcare 2023, 11(9), 1268; https://doi.org/10.3390/healthcare11091268 - 28 Apr 2023
Cited by 10 | Viewed by 4128
Abstract
Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information [...] Read more.
Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field. Full article
(This article belongs to the Special Issue Data Mining and Sentiment Analysis in Healthcare)
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