International Journal of Electronic Healthcare (5 papers in press)

Regular Issues

• Discovering patterns of live birth occurrence before in vitro fertilization treatment using association rule mining  
  by Kamal Upreti, Divakar Singh, Anju Singh, Prashant Vats, Rishu Bhardwaj, Shreya Kapoor 
  Abstract: According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF market’s rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans.
  Keywords: in-vitro fertilisation; IVF; association rule mining; ARM; market basket analysis; donor; artificial intelligence; AI.
  DOI: 10.1504/IJEH.2023.10061879
   
  
• Monetization strategies for health apps: evidence from Apple’s App Store and Google Play Store  
  by Natália Lemos, Claudia Cardoso, Cândida Sofia Machado 
  Abstract: Monetisation models of multilateral platforms are key elements for the viability and success of health apps. In this paper, we study how alternative monetisation methods influence the price of health apps. For a sample of health apps available on the Apple’s App Store and the Google Play Store, we use a censored regression model to identify the factors that influence the expected price of a health app. Our results show that the presence of alternative monetisation mechanisms negatively affects the expected price of a health app. Monetisation mechanisms based on ad revenue are those with the strongest impact on lowering the download price of health apps, followed by monetisation mechanisms based on the monetisation of
  Keywords: monetisation models; price level; pricing structure; multilateral markets; health apps; digital transformation; e-health; m-health; ads revenue.
  DOI: 10.1504/IJEH.2023.10062337
   
  
• Performance Analysis of 3D Brain MRI Modalities for Brain Tumor Sub-Region Segmentation Using U-Net Architecture  
  by Anima Kujur, ZAHID RAZA 
  Abstract: Magnetic resonance imaging (MRI) is the most widely used technology for brain tumour diagnosis. T1, T1-weighted with and without a contrast agent, fluid-attenuated inversion recovery (FLAIR), and T2-weighted are some of the numerous MRI imaging types available. This work proposes a framework and measures its effectiveness while taking into account the contribution made by each MRI modality in the segmentation of brain tumour sub-regions. The proposed approach has four phases. First phase corresponds to the data collection followed by data pre-processing in the second phase. The third phase entails the implementation of a deep learning model called the U-Net architecture, a form of convolutional neural networks (CNN). The fourth phase evaluates the framework using the popular measures, viz., intersection over union, dice coefficient for each tumour region, sensitivity, and specificity. Simulation study reveals that some MRI modalities contribute comparatively less than the peer MRI modalities to the final performance of the model. Further, it is established that MRIs individually do not perform well but the combination reports an improved performance.
  Keywords: magnetic resonance imaging; MRI; medical image segmentation; convolutional neural network; CNN; U-Net; deep learning; fluid-attenuated inversion recovery; FLAIR.
  DOI: 10.1504/IJEH.2023.10062485
   
  
• Analysis of Sentiments in E-health Trends of Twitter Data  
  by Vibha Prabhu, Rajesh R. Pai, Sumith Nire, Anushka Pandey 
  Abstract: Sentiment analysis (opinion mining) is a natural language processing (NLP) technique used to determine the polarity of text - whether it’s positive, negative, or neutral. The objective of this paper is to conduct sentiment analysis utilising social media ‘X’ (formerly Twitter), focusing on healthcare-related data. The findings imply that e-health is viewed favourably. Furthermore, our analysis show that the most common hashtags connected with e-health on ‘X’, ranged from health technology to health information. The project pipeline includes tweet extraction using different hashtags, processing text, feature extraction, visualisation and testing accuracy of tweet sentiments using different classifiers. Tools like pandas, tweepy, NumPy, text blob, Vader, etc. have been used for this analysis. The ultimate goal is to enhance healthcare standards through effective utilisation of streamlined data collection methods in predictive analytics, aiming to boost daily operations and proactive patient care.
  Keywords: sentiment analysis; e-health; machine learning; opinion mining; Twitter mining.
  DOI: 10.1504/IJEH.2023.10062486
   
  
• A deep learning neural network strategy for secure wireless communication in electronic health record surveillance  
  by T. Senthil Kumar, L. Mohana Sundari, R. Muthalagu, K. Periyarselvam 
  Abstract: The process of monitoring electronic health records (EHRs) depends on wireless security that is managed through application technologies, such as the internet of things (IoT). In view of the growing availability of medical narratives in the electronic health record, automated monitoring would be a workable way to improve treatment. This would take into account the growing accessibility of medical records. Patients who use electronic health records (EHRs) in general need to bear in mind how important security is. The cumulative management of security across access, modification, and storage is accomplished via the use of the common paradigm. This article presents an authorised joint security system (AJSS) with the intention of enhancing the level of security that is present in the aforementioned monitoring services. The process of monitoring electronic health records (EHRs) depends on wireless security that is managed through internet of things (IoT).
  Keywords: security; electronic health record; EHR; federated learning; internet of things; IoT.
  DOI: 10.1504/IJEH.2023.10063376