WOS:001033976100001Text analytics and natural language processing (NLP) have emerged as powerful tools in healthcare, revolutionizing patient care, clinical research, and public health administration. Over the years, as healthcare databases expand exponentially, healthcare providers, pharmaceutical and biotech industries are utilizing both tools to enhance patient outcome

Kadir Sabanci

Khairunnisa Hasikin

Khin Wee Lai

Muhammet Fatih Aslan

Suresh Chandra Satapathy

Frontiers in Digital Health

Hasikin, Khairunnisa

Lai, Khin Wee

Satapathy, Suresh Chandra

Sabancı, Kadir

Aslan, Muhammet Fatih

DSpace@KMU

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

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TYPE EditorialPUBLISHED 23 June 2023| DOI 10.3389/fdgth.2023.1227948EDITED AND REVIEWED BYSadiq Hussain,Dibrugarh University, India*CORRESPONDENCEKhairunnisa Hasikinkhairunnisa@um.edu.myRECEIVED 24 May 2023ACCEPTED 14 June 2023PUBLISHED 23 June 2023CITATIONHasikin K, Lai KW, Satapathy SC, Sabanci K andAslan MF (2023) Editorial: Emergingapplications of text analytics and naturallanguage processing in healthcare.Front. Digit. Health 5:1227948.doi: 10.3389/fdgth.2023.1227948COPYRIGHT© 2023 Hasikin, Lai, Satapathy, Sabanci andAslan. This is an open-access article distributedunder the terms of the Creative CommonsAttribution License (CC BY). The use,distribution or reproduction in other forums ispermitted, provided the original author(s) andthe copyright owner(s) are credited and that theoriginal publication in this journal is cited, inaccordance with accepted academic practice.No use, distribution or reproduction ispermitted which does not comply with theseterms.Frontiers in Digital HealthEditorial: Emerging applications oftext analytics and natural languageprocessing in healthcareKhairunnisa Hasikin1,2*, Khin Wee Lai1, Suresh Chandra Satapathy3,Kadir Sabanci4 and Muhammet Fatih Aslan41Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,2Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya,Kuala Lumpur, Malaysia, 3School of Computer Engineering, Kalinga Institute of Industrial Technology,Deemed to Be University, Bhubaneswar, India, 4Department of Electrical and Electronics Engineering,Karamanoglu Mehmetbey University, Karaman, TurkeyKEYWORDStext processing, machine learning (ML), deep learning, Word2Vec analysis, artificialintelligence, LSTM (Long short term memory networks)Editorial on the Research TopicEmerging applications of text analytics and natural language processing inhealthcareText analytics and natural language processing (NLP) have emerged as powerful tools inhealthcare, revolutionizing patient care, clinical research, and public health administration.Over the years, as healthcare databases expand exponentially, healthcare providers,pharmaceutical and biotech industries are utilizing both tools to enhance patientoutcomes. In addition, the proliferation of technologies for wearable devices has openednew opportunities to exploit the consumer health data. For example, Khairuddin et al.(1) proposed a multimodal input data combining the text features and numerical data ofoccupational safety and health management system in reducing workplace injury cases. Inaddition, the patients’ clinical data were also included with the breast thermal images inthe predictive model development of breast cancer detection (2). In this research topic,the publications have been rigorously peer-reviewed by external reviewers with strongbackground of text analytics and innovations in the NLP research especially in healthcareapplications. Publications on this research topic have been subjected to rigorous peerreview by external reviewers with a solid background in text analytics and innovations inNLP research, with a focus on healthcare applications.Among the published works in this Research Topic, three research groups focused on theemerging applications of text analytics and NLP in medical/clinical applications. Zhou et al.(3) proposed an ensemble transfer learning approach to oncological field named BiomedicalNamed Entity Recognition (NER) in Chinese clinical records. Using an ensemble approach,the researchers combine multiple models to improve the efficacy of the NER system. The firststage of the model consists of the pre-trained Word2Vec and Bidirectional EmbeddingsRepresentations to attain domains-specific word embeddings. These are then used as aninput to the second stage that consists of Bidirectional Long and Short Time MemoryRecurrent Neural Network (BiLSTM) and Linear-chain Conditional Random Field (CRF).By combining multiple models, each with their own strengths and weaknesses, theensemble seeks to produce more precise and reliable results. In addition, the authors01 frontiersin.orgHasikin et al. 10.3389/fdgth.2023.1227948employ augmented domain resources, such as additional labelleddata or external oncology-related knowledge sources, to furtherimprove the performance of the NER system.The other group focus on integrating text analytics ondynamic multimodal feature recognition for mental healthassessment (Xu et al.). The research incorporating interactiveassessment scales containing voice interaction, videoacquisition, and statistical analysis modules with theDepression Anxiety Stress Scales (DASS-21). The proposedmethod entails capturing and analysing various characteristicsfrom various modalities which include acoustic characteristicsof speech, and facial expressions from video recordings. Themethod attempts to capture a more comprehensiveunderstanding of an individual’s mental state by incorporatingmultiple modalities. Through dynamic multimodal featurerecognition, the identification of patterns, changes, oranomalies that can reveal information about a person’s mentalhealth status were enabled. The study recruited 1,500participants for this mental health evaluation, in which thefacial video model and the audio emotion detection modelwere separately fed to the convolutional neural networkarchitecture. Then, each output was fed to the LSTM, wheremultimodal data fuses potentially shared information of eachmodality and compares emotion recognition results using anenumeration-weighted decision fusion strategy based onstatistical rules and probability theory.Additionally, the other research group solely focused on thenatural language processing technology in medical rehabilitation(Wang and Sun). This study highlights the growing uses of textanalytics and natural language processing in the healthcareindustry, specifically in the context of enhancing physical fitnessevaluation and rehabilitation procedures for teenagers who arephysically handicapped. According to the findings of the study, itis critical to have policies that are both comprehensive and wellimplemented in order to meet the requirements of impairedpeople and to foster their growth. This study investigates theconnection between physically challenging exercise and positivemental health in young people who have a physical disability,using NLP technology. The findings provide importantinformation for medical practitioners and policymakers. Thefindings provide light on the obstacles that are faced byadolescents who are physically challenged and emphasisethe need of individualised rehabilitation programmes to promotethe overall well-being of physically disabled adolescents. Theemergence of these new applications of text analytics and naturallanguage processing demonstrates the potential of these twofields to develop medical practises and foster inclusivity in society.In another application of text analytics can be seen in thepublished works by Sang and Chen. This research group hasleveraged the text analytics capabilities in developing anintelligent and fully automated physical education teaching mode.The human-computer interaction based on speech recognitiontechnology was combined with physical education teaching. Theresearch demonstrated that the voice cues from the students canbe analysed and recognised by their proposed system which thensends information back to the students in different ways. ThisFrontiers in Digital Health 02work implements the Mel cepstral coefficient technique forprocessing the external speech signal. The promising results fromthis research has enabled the emergence of human-computerinteraction to cultivate active learning.The extensive works conducted by Eom and Byeon shed lighton the significant impact of COVID-19 pandemic on obesitytrends in Korea. The research makes good use of text analyticsand natural language processing techniques in order to extractuseful insights from massive amounts of news data. Thefindings underscore the dynamic character of public healthconcerns and the significance of regularly monitoring andtackling developing factors that contribute to obesity. Thefindings also emphasise the fact that public health concernsare constantly evolving. The incorporation of such applicationsof text analytics and natural language processing intohealthcare has the potential to facilitate the making ofdecisions based on evidence and assist in more effectivelyaddressing ever-evolving health concerns.During COVID-19 pandemic has also nurtured relatedresearch on text analytics and NLP. A scoping review of theexisting landscape of AI-based applications in clinical trials wasundertaken in one of the articles that was published in thisResearch Topic. The findings of this study highlight the growingapplicability of text analytics and natural language processing inthe medical field, particularly in light of the COVID-19pandemic. The relevance of social media platforms as powerfulsources of public opinion and emotional expression ishighlighted in the study. The proposed approach uses techniquesfrom sentiment analysis and machine learning to enable therecognition of real news relating to COVID-19 in Arabiclanguage, with a particular emphasis on Gulf countries. Thefindings, which revealed that a pervasive negative feeling wasexperienced throughout the epidemic, underscore the need ofmonitoring public emotions and opinions in order to supporteffective policymaking and control efforts. These newly emerginguses of text analytics and natural language processing provideinsightful information for healthcare professionals andpolicymakers, which makes it easier to implement timelyinterventions and response tactics during times of public healthcrises.To summarise, the increasing applications of text analyticsand NLP show a great deal of potential in changing theprocesses involved in providing medical care. By utilising thesetechnologies, medical professionals and policymakers are ableto improve their ability to make judgements, obtain more in-depth insights, and, as a result, improve the quality oftreatment that is provided to patients. This has the potentialto have a positive impact on the outcomes for patients as wellas contribute to the general improvement of the field ofhealthcare in general.Author contributionsKH have provided comprehensive overview of the emergingapplications of text analytics and natural language processingfrontiersin.orgHasikin et al. 10.3389/fdgth.2023.1227948(NLP) in healthcare. KL have summarized and analysed tworesearch studies hat exemplify the potential of thesetechnologies in addressing healthcare challenges. SS and KShave contributed to the editorial by providing context andinsights into the significance of the research studies. MA hascarefully reviewed and synthesized the abstracts of the selectedstudies, highlighting the key findings, methodologies, andimplications. In crafting the editorial, the authors havesynthesized complex research concepts into clear and conciselanguage, ensuring that the content is accessible to a widerange of readers. They have provided an informative andengaging conclusion, summarizing the key takeaways from thediscussed studies and highlighting the future directions andpotential challenges in the field. All authors contributed to thearticle and approved the submitted version.Frontiers in Digital Health 03Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Publisher’s noteAll claims expressed in this article are solely those of theauthors and do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteedor endorsed by the publisher.References1. Khairuddin MZF, Lu Hui P, Hasikin K, Abd Razak NA, Lai KW, Mohd Saudi AS,et al. Occupational injury risk mitigation: machine learning approach and featureoptimization for smart workplace surveillance. Int J Environ Res Public Health.(2022) 19(21):13962. doi: 10.3390/ijerph1921139622. Mammoottil MJ, Kulangara LJ, Cherian AS, Mohandas P, Hasikin K, MahmudM. Detection of breast cancer from five-view thermal images usingconvolutional neural networks. J Healthc Eng. (2022) 2022:4295221. doi: 10.1155/2022/42952213. Zhou M, Tan J, Yang S, Wang H, Wang L, Xiao Z. Ensemble transfer learning onaugmented domain resources for oncological named entity recognition in Chineseclinical records. Front Public Health. (2023) 11:992330. doi: .org/10.3389/fpubh.2023.992330frontiersin.org

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

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