Research challenges and future directions towards medical data processing

Ampavathi, Anusha; T, Vijaya Saradhi

doi:10.1080/21681163.2021.2018665

Cited by 6 publications

(8 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, to improve the performance of health systems, we need to increase the available resources, and develop visions and joint statements to improve health care using artificial intelligence technologies. [34][35][36] The limitations of the current research were as follows: One assumption that was not taken into account was related to population and traffic data, which, depending on the geographical and climatic conditions, may affect the speed of the vehicle and the time-of-service delivery, thus complicating the whole process. In addition, the differences in diseases were also not considered.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Khalilzadeh,

Bahari

2023

Health�Serv�Insights

View full text Add to dashboard Cite

To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Khalilzadeh,

Bahari

2023

Health�Serv�Insights

View full text Add to dashboard Cite

show abstract

Section: What Are the Gaps?mentioning

confidence: 99%

“…There has been an increase in research and research needs discussion highlighting possibilities for the future development of artificial intelligence in nursing care. However, the importance of collaborative, interdisciplinary research has been generally underscored (Ampavathi & Vijaya Saradhi, 2022 ; Apell & Eriksson, 2021 ; Jacennik, 2022 ).…”

Section: Artificial Intelligence Priorities For Nursing Management: T...mentioning

confidence: 99%

“…Further to the gaps for practice identified, a further critical gap identified has also been clarified and it concerns research on the limitations of the clinical storage systems, noise removal methods and multi‐disease prediction models (Ampavathi & Vijaya Saradhi, 2022 ). As these are related to innovation system performance, gaps are primarily restricted by the system weaknesses of limited resources and insufficient communication from leading health care professionals regarding their needs for improving health care using artificial intelligence technology innovations.…”

Section: What Are the Gaps?mentioning

confidence: 99%

See 1 more Smart Citation

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Chen

Moreira

Liu

et al. 2022

J Nursing Management

View full text Add to dashboard Cite

Aim The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers. Background Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision‐making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions. Evaluation Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then. Key Issue The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management. Conclusion We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature. Implications for Nursing Management Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.

show abstract

Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

Vonghirandecha,

Kansomkeat,

Bhurayanontachai

et al. 2023

Computer Methods in Biomechanics and Biomedical Engineering: Im

View full text Add to dashboard Cite

Research challenges and future directions towards medical data processing

Cited by 6 publications

References 111 publications

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

Contact Info

Product

Resources

About