Lstm Neural Networks and Iot Data for Predictive Maintenance in Healthcare

Abstract

The most important in the modern provision of health care are medical devices that are involved in the process of prevention, diagnosis and treatment, rehabilitation. Ensuring their proper technical condition is the key to patient and user safety. However, the traditional ways of maintaining medical equipment are not enough for the increasing complexity of devices. By using information technology, social networking technologies, computerized systems digitization, and big data analytics, including machine learning, we have the ability to improve the quality of provision of services in the healthcare system. Predictive maintenance has become a fast-growing trend for assessing the technical condition of equipment and making predictions about possible failure scenarios to organize preventive maintenance. This systematic literature review will analyze previous research on predictive maintenance, with a special focus on its use in healthcare. The analysis of the articles found in several scientific search databases demonstrates that there is still much untapped potential for predictive maintenance in healthcare. This paper aims to introduce a new approach tuple, which will make it possible to provide proactive maintenance of medical equipment with the use of long short-term memory and Internet of things in healthcare analytics. This SLR will serve as a starting point to understand the predictive maintenance solutions in the industry, main findings, challenges, and new opportunities, and will give insights for future research regarding predictive maintenance.