Semantic Information Retrieval on Medical Texts

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.

Data Checkers: A Grid-Based UI for Managing Patient-Generated Data Sharing to Support Collaborative Self-Care

Chronic health conditions are becoming increasingly prevalent. As part of chronic care, sharing patient-generated health data (PGHD) is likely to play a prominent role. Sharing PGHD is increasingly recognized as potentially useful for not only monitoring health conditions but for informing and supporting collaboration with caregivers and healthcare providers. In this paper, we describe a new design for the fine-grained control over sharing one's PGHD to support collaborative self-care, one that centers on giving people with health conditions control over their own data. The system, Data Checkers (DC), uses a grid-based interface and a preview feature to provide users with the ability to control data access and dissemination. DC is of particular use in the case of severe chronic conditions, such as spinal cord injuries and disorders (SCI/D), that require not just intermittent involvement of healthcare providers but daily support and assistance from caregivers. In this paper, after providing relevant background information, we articulate our steps for developing this innovative system for sharing PGHD including (a) use of a co-design process; (b) identification of design requirements; and (c) creation of the DC System. We then present a qualitative evaluation of DC to show how DC satisfied these design requirements in a way that provided advantages for care. Our work extends existing research in the areas of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Ubiquitous Computing (Ubicomp), and Health Informatics about sharing data and PGHD.

A Stochastic Model for Kala-azar Transmission Dynamics in Libo Kemkem, Ethiopia

The objective of this paper is to analyse and demonstrate the dynamics of Kala-azar infected group using stochastic model, particularly using simple SIR model with python script over time. The model is used under a closed population with N = 100, transmission rate coefficient β = 0.09, recovery rate γ = 0.03 and initial condition I(0) = 1. In the paper it is discussed how the Kala-azar infected group behaves through simple SIR model. The paper is completed with stochastic SIR model simulation result and shows stochasticity of the dynamics of Kala-azar infected population over time. Fig. 2 below depicts continuous fluctuations which tells us the disease evolves with stochastic nature and shows random process.Subject: Infectious Disease, Global Health, Health Informatics and Statistical and Computational Physics

Automation in Healthcare

Major challenges to the society are the people have aging populace and occurrence of continual diseases and eruption of transferable diseases. to embark upon these unmet healthcare desires for the quick guess and therapeutic of all the important diseases a new area called health informatics is emerging as an interdisciplinary research which is dealing with the getting hold of, spread, dispensation, to store as well retrieve. Particularly when the industry is acquired the health information by using the unassuming sense and wearable technology is well thought-out as groundwork stone in healthiness industry. According to a reports, sensors can be worn and hooked on clothes which can acquire the health information uninterrupted.

Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak

Health informatics is an interdisciplinary field in the computer and health sciences. Health informatics, which enables the effective use of medical information, has the potential to reduce both the cost and the burden of healthcare workers during the pandemic process. Using the machine learning algorithms support vector machines, naive bayes, k-nearest neighbor, and C4.5 algorithms, a model performance evaluation was performed to identify the algorithm that will show the highest performance for the prediction of the disease. Three separate training and test datasets were created 70% - 30%, 75% - 25%, and 80% - 20%, respectively. The implementation phase of the study was carried out by following the CRISP-DM steps, and the analyses were made using the R language. By examining the model performance evaluation criteria, the findings show that the C4.5 algorithm showed the best performance with 70% training dataset.

Public Health Informatics in Global Health Surveillance: A Review

Background: Surveillance is the backbone for effective public health practice. Traditionally, surveillance system relies on the collection of information regarding health-related events through healthcare facilities, disease notification system from the physician, syndromic notification networks, selected sentinel healthcare facilities, or by event-based data. However, there are several limitations in using conventional surveillance. Methods: With the advancement of technology and computer science, overcoming those limitations and complementing the traditional method has been recommended. Three leading emerging technologies are applied in public health surveillance: the internet of things, artificial intelligence, and blockchain. Results: Application of informatics in public health surveillance could raise several issues including accessibility and affordability of innovations; public health informatics’ experts, law, and regulation to protect patients’ information; social and ethical considerations, norms, and standards of implementing new technologies; data ownership; privacy and sharing of information; biosecurity; biosafety; and cybersecurity. Conclusion: This article aimed to review several applications of informatics system in public health surveillance practice and its several issues related to the use of technology. Several applications of informatics could be useful for incoming challenges in public health. However, application of informatics can pose significant issues and must be taken into consideration in public health practice.