Different clinics and hospitals have their own information systems to maintain patient data. This hinders the exchange of data among systems (and organizations). Hence there is a need to provide standards for data exchange. In digitized form, the individual patient's medical record can be stored, retrieved, and shared over a network through enhancement in information technology. Thus, electronic health records (EHRs) should be standardized, incorporating semantic interoperability. A subsequent step requires that healthcare professionals and patients get involved in using the EHRs, with the help of technological developments. This study aims to provide different approaches in understanding some current and challenging concepts in health informatics. Successful handling of these challenges will lead to improved quality in healthcare by reducing medical errors, decreasing costs, and enhancing patient care. The study is focused on the following goals: (1) understanding the role of EHRs; (2) understanding the need for standardization to improve quality; (3) establishing interoperability in maintaining EHRs; (4) examining a framework for standardization and interoperability (the openEHR architecture; (5) identifying the role of archetypes for knowledge-based systems; and (6) understanding the difficulties in querying HER data.

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.

The current research explores and differentiates between various forms in which NoSQL databases exist. It examines the need of NoSQL and how they have become an important alternative to relational databases. NoSQL databases can be categorized into four major classifications which are: key value stores, graph databases, wide column stores, and document stores. These categories are compared on the basis of functional features and non-functional features. The non-functional features include performance, scalability, flexibility, structure and complexity. The functional features include de-normalization, joins, atomicity, aggregation and keys. Then for further analysis, one database is selected from each of these categories that is, MongoDB (document stores), Cassandra (wide column stores), Redis (key value stores), and Neo4j (graph databases). Selected databases are compared on their data model, CAP theorem, distributive properties and other factors. By performing the comparison on non-functional features, it has been found that a document store can be used if high performance, flexibility and scalability are required and if we have represented the data in JSON format. Column store can be used for semi structured data which requires high performance and scalability. Redis is anin-memory store and performs exceptionally fast in the case of single shard operation. Graph databases can be used when it comes to highly interconnected data and continuously evolving data models. The comparison between MongoDB, Cassandra, Redis and Neo4j concluded that all of them follow horizontal scaling and are schema free. Except Neo4j, others don't have complete ACID properties. Write and delete operations are fast for databases MongoDB, Redis and Cassandra, whereas read operation is comparatively slow in Cassandra. In case of Neo4j, REST performance is similar to MongoDB, whereas embedded is comparatively slow. We also discuss how these databases work in a distributed environment.

Deep learning has achieved high performance in many fields like image classification, object detection etc. Recently many researchers have tried to carry out deep learning in medical image analysis. Convolutional Neural Network (CNN) has been set as a profound class of models in this field. It is a deep learning model which extracts the features of an image and then classify it. In this study, an analysis of Breast Cancer (BC) histopathology images is done using famous CNN architecture “AlexNet”. Histopathology images are the gold measure for the breast cancer diagnosis. Using deep learning for predicting breast cancer can prove to be very much effective in near future. Here a dataset, which consists 7909 images of 82 patients is used to train our model and later the image is successfully being classified. Impressive results and analysis are also achieved using this approach.