International Journal of Healthcare Information Systems and Informatics 

About: International Journal of Healthcare Information Systems and Informatics is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Health care & Information system. It has an ISSN identifier of 1555-3396. Over the lifetime, 356 publications have been published receiving 3303 citations. The journal is also known as: Healthcare information systems and informatics.

Content-Based Image Retrieval in Medicine: Retrospective Assessment, State of the Art, and Future Directions

Abstract: Content-based image retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practical medical problems is a goal yet to be realized. Furthermore, we highlight “gaps” between desired CBIR system functionality and what has been achieved to date, present for illustration a comparative analysis of four state-of-the-art CBIR implementations using the gap approach, and suggest that high-priority gaps to be overcome lie in CBIR interfaces and functionality that better serve the clinical and biomedical research communities.

A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique

Abstract: Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques: An Algorithmic Approach for Early Screening of Diabetes:

Abstract: Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.

Improving Opportunities in Healthcare Supply Chain Processes via the Internet of Things and Blockchain Technology

Abstract: Despite key advances in healthcare informatics and management, little progress to address supply chain process-related problems has been made to date. Specifically, key healthcare supply chain processes include product recalls, monitoring of product supply shortages, expiration, and counterfeits. Implementing and executing these processes in a trusted, secure, efficient, globally accessible and traceable manner is challenging due to the fragmented nature of the healthcare supply chain, which is prone to systemic errors and redundant efforts that may compromise patient safety and impact health outcomes adversely. Blockchain, combined with the Internet of things (IoT), is an emerging technology that can offer a practical solution to these challenges. Accordingly, IoT blockchain offers a superior way to track and trace products via a peer-to-peer distributed, secure, and shared ledger of the blockchain network. This article highlights key challenges related to healthcare supply chains, and illustrates how IoT blockchain technologies can play a role in overcoming these challenges now and in the near future.

Evolution of Information Systems and Technologies Maturity in Healthcare

Abstract: Information Systems and Technologies IST in healthcare have evolved gradually, and theories about IST adoption and maturity are sufficiently established in the literature of organizational management. This paper examines the evolution of IST in healthcare. The author introduces concepts associated with maturity models, addresses the generic maturity model for IST management, and presents the main maturity models, specifically focusing on the management of IST in healthcare. Widespread and detailed maturity models are not fully available, and the opportunity to develop new maturity models that focus on IST management in healthcare still exists.