Machine Learning For Predictive Analytics Research Articles

Secure Cloud Services for the Healthcare Industry: Addressing Unique Challenges and Ensuring Compliance

Abstract: The healthcare industry is increasingly adopting cloud computing to manage patient data, provide telehealth services, and support remote work. However, this adoption presents unique challenges due to stringent regulations governing the handling of personal health information (PHI), such as HIPAA in the United States and GDPR in the European Union. Noncompliance with these regulations can result in significant financial penalties, reputational damage, and loss of patient trust. This article explores the specific data security needs of the healthcare industry, the challenges in adopting cloud services, and the essential features of secure cloud services tailored for healthcare. It also discusses the integration of artificial intelligence (AI) and automation in cloud services to enhance data security and operational efficiency, as well as future trends in cloud computing for healthcare, such as the increasing adoption of AI and machine learning for predictive analytics and automated threat detection. By providing a comprehensive overview of the data security needs, challenges, and solutions in adopting cloud services for healthcare, this article aims to assist healthcare organizations in making informed decisions when considering the adoption of cloud services and to highlight the importance of prioritizing data security and compliance in the digital transformation of the healthcare industry.

Coupling simulation and machine learning for predictive analytics in supply chain management

Predictive analytics is the approach to business analytics that answers the question of what might happen in the future. Although predictive information is critical for making forward-looking decisions, traditional approaches struggle to cope with the increasing uncertainty and complexity that characterise modern supply chains. Simulation is limited by insufficient timeliness, while machine learning is constrained by poor interpretability and data scarcity. Inspired by the complementary nature of simulation and machine learning, an integrated predictive analytics approach is proposed and applied to a humanitarian supply chain. By coupling simulation and machine learning, predictive models can be developed with limited historical data, and pre-crisis performance assessment can be performed to facilitate timely and informed decisions. The proposed approach enables managers to gain valuable insights into the complex evolution of the uncertain future, which also opens up the possibility of further integration with optimisation and digital twins.

Clinical Decision Support Systems Leverage Machine Learning for Predictive Analytics — Part 2

Part one of this article published in July 2019 focused on two major application areas of Machine Learning (ML) in Clinical Decision Support Systems (CDSS) i.e., 1) Computer Aided Diagnosis (CAD) and prognosis (progression) and 2) Risk stratification and preventive healthcare. In this second part of the article, we continue to discuss more cutting-edge ML applications in CDSS including clinical pathways optimization, diagnosis and personalized medicine development based on genomics and related data.

Clinical Decision Support Systems Leverage Machine Learning for Predictive Analytics — Part 1

Medical practice has always remained at the forefront of data-driven decision-making. For instance, primary care physicians have commonly used several types of risk scores and diagnostic data to predict morbidity and mortality in their patients. However, with Terabyte (TB) size structured and unstructured datasets abound, a massive shift is underway in the clinical decision support scene. The costs, efficiency, and effectiveness of decision-making for care planning, diagnosis, treatment, adherence monitoring and management of patient health outcomes have been improving at unprecedented rates in the last two decades.