Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical ...

Now that the technology is more widely available than ever before, protecting information assets is paramount, especially in the face of escalating cyber threats. The healthcare sector has experienced a significant increase in cyber-attacks, ...

Machine learning becomes more prevalent in specialized domains such as medicine and biology every year, but domain expert trust in machine learning continues to lag behind. Researchers have explored increasing rational trust in AI but little research ...

With the advent of 5G and the era of Big Data, the rapid development of medical information technology around the world, the massive application of electronic medical records and cases, and the digitization of ...

Securing and managing medical data in hospitals is one of the significant challenges still existing in healthcare. There can be different kinds of patients staying in hospitals with various diseases. All these medical data records need to be secured ...

With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloud‐based hospital information system, for access control mediation over a variety of ...

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in the medical field. Two critical challenges are identified: First, ...

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages ...

Class imbalance of medical records is a critical challenge for disease classification in intelligent diagnosis. Existing machine learning algorithms usually assign equal weights to all classes, which may reduce classification accuracy of ...

In recent times, large amount of medical data is being generated by various sources such as test reports, medications, etc. Due to the recent advances of machine learning (ML) and deep learning (DL) models, medical data classification (MDC) remains a ...

Electronic medical data management is eased with the integration of communication technologies and the cloud/Internet of Things (IoT) platform in recent years. The organisation and mining of the data from massive repositories is a complex and time-...

Internet of things (IoT) is the catchphrase in the recent years with transdisciplinary research. Medical internet of things (M-IoT) is the novel development in the fields of healthcare and information technology to store and retrieve the medical data that ...

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, ...

Medical ontologies are widely used to describe and organize medical terminologies and to support many critical applications on healthcare databases. These ontologies are often manually curated (e.g., UMLS, SNOMED CT, and MeSH) by medical experts. ...

Increasing digitization in healthcare promises easier exchange and more efficient use of medical information for patients, institutions and research. The number of sharing options for medical data increases, e.g., through personal health records, as ...

The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and ...

Machine Learning, and in particular Federated Machine Learning, opens new perspectives in terms of medical research and patient care. Although Federated Machine Learning improves over centralized Machine Learning in terms of privacy, it does not provide ...

COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to a treacherous and devastating catastrophe for humanity. No specific antivirus drugs or vaccines are recommended to control infection transmission and spread at the time of ...

At present, with the gradual expansion of the application scope of big data technology, its application frequency in medicine is becoming more and more frequent. The purpose of using medical data for different projects is mainly to reduce the pressure of ...

With the increase of unlabeled data in medical datasets, the labelling process becomes a more costly task. Therefore, active learning provides a framework to reduce the amount the manual labour process by querying an expert for just the labels of ...