Emerging Applications of Text Analytics and Natural Language Processing in Healthcare

Editorial

23 June 2023

Editorial: Emerging applications of text analytics and natural language processing in healthcare

Khairunnisa Hasikin

Khin Wee Lai

Suresh Chandra Satapathy

Kadir Sabanci

and

Muhammet Fatih Aslan

2,641 views

3 citations

Editors

Khairunnisa Hasikin

Department of Biomedical Engineering, Faculty of Engineering, University of Malaya

Karamanoğlu Mehmetbey University

Impact

Original Research

10 October 2022

Twitter sentiment analysis: An Arabic text mining approach based on COVID-19

Saleh Albahli

The proposed sentiment analysis pipeline.

The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.

5,571 views

12 citations

Interactive multimodal emotion recognition scales system. The system includes two parts: an interactive questionnaire process and a multimodal emotion recognition process.

Original Research

23 December 2022

A measurement method for mental health based on dynamic multimodal feature recognition

Haibo Xu

, 1 more and

Xin Liu

4,159 views

7 citations

Review

12 August 2022

Scoping review of the current landscape of AI-based applications in clinical trials

Fidelia Cascini

, 4 more and

Walter Ricciardi

Background: Clinical trials are essential for bringing new drugs, technologies and procedures to the market and clinical practice. Considering the design and the four-phase development, only 10% of them complete the entire process, partly due to the increasing costs and complexity of clinical trials. This low completion rate has a huge negative impact in terms of population health, quality of care and health economics and sustainability. Automating some of the process' tasks with artificial intelligence (AI) tools could optimize some of the most burdensome ones, like patient selection, matching and enrollment; better patient selection could also reduce harmful treatment side effects. Although the pharmaceutical industry is embracing artificial AI tools, there is little evidence in the literature of their application in clinical trials.

Methods: To address this issue, we performed a scoping review. Following the PRISMA-ScR guidelines, we performed a search on PubMed for articles on the implementation of AI in the development of clinical trials.

Results: The search yielded 772 articles, of which 15 were included. The articles were published between 2019 and 2022 and the results were presented descriptively. About half of the studies addressed the topic of patient recruitment; 12 articles reported specific examples of AI applications; five studies presented a quantitative estimate of the effectiveness of these tools.

Conclusion: All studies present encouraging results on the implementation of AI-based applications to the development of clinical trials. AI-based applications have a lot of potential, but more studies are needed to validate these tools and facilitate their adoption.

9,853 views

30 citations