Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

Shah, Adnan Muhammad; Yan, Xiangbin; Tariq, Samia; Shah, Syed Asad Ali

doi:10.36909/jer.v9i3a.8703

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…This could involve identifying relevant healthcare topics in post-COVID-19 literature and quantifying the frequency and sentiment of those topics. In addition, NLP techniques were used to identify relationships between topics within the literature [99,172,173]. This could provide insights into how healthcare topics have changed after the COVID-19 pandemic.…”

Section: Limitations and Future Workmentioning

confidence: 99%

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

2023

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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.

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Section: Limitations and Future Workmentioning

confidence: 99%

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

2023

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“…The COVID-19 pandemic is an infectious disease that occurred in Wuhan, China, in December 2019 ( Yum, 2020 ). The virus that causes the pandemic is transmitted quickly by breathing or close contact ( Shah et al, 2021 ). The person suffering from the disease does not experience an individual illness or an individual situation.…”

Section: Introductionmentioning

confidence: 99%

Social network analysis of tourism data: A case study of quarantine decisions in COVID-19 pandemic

Altuntaş¹,

Altuntaş²,

Dereli³

2022

International Journal of Information Management Data Insights

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“…Understanding patient preferences of service quality is vital for the healthcare industry and healthcare providers to develop optimal strategies to improve patients' quality of care [ 1 ]. With the growing popularity of physicians rating websites (PRWs), better information can be obtained regarding factors influencing patients' choices of selecting the right doctor [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

Shah

Muhammad

Lee

2022

Computational Intelligence and Neuroscience

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(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.

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Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

Cited by 4 publications

References 29 publications

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

Social network analysis of tourism data: A case study of quarantine decisions in COVID-19 pandemic

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

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