Use of cloud computing in biomedicine

Medicare meets the cloud: the development of a secure platform for the storage and analysis of claims data

Introduction

Cloud-based solutions are a modern-day necessity for data intense computing. This case report describes in detail the development and implementation of Amazon Web Services (AWS) at Emory-a secure, reliable, and scalable platform to store and analyze identifiable research data from the Centers for Medicare and Medicaid Services (CMS).

Materials and Methods

Interdisciplinary teams from CMS, MBL Technologies, and Emory University collaborated to ensure compliance with CMS policy that consolidates laws, regulations, and other drivers of information security and privacy.

Results

A dedicated team of individuals ensured successful transition from a physical storage server to a cloud-based environment. This included implementing access controls, vulnerability scanning, and audit logs that are reviewed regularly with a remediation plan. User adaptation required specific training to overcome the challenges of cloud computing.

Conclusion

Challenges created opportunities for lessons learned through the creation of an end-product accepted by CMS and shared across disciplines university-wide.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Cardiovascular Informatics: building a bridge to data harmony

The search for new strategies for better understanding cardiovascular disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in cardiovascular biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and cardiovascular medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to cardiovascular biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of Cardiovascular Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently-formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of cardiovascular (CV) diseases and unification of cardiovascular knowledge.

Towards a European health research and innovation cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Novel Cross-View Human Action Model Recognition Based on the Powerful View-Invariant Features Technique

One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques.

Collaborative writing applications in healthcare: effects on professional practice and healthcare outcomes

BACKGROUND

Collaborative writing applications (CWAs), such as wikis and Google Documents, hold the potential to improve the use of evidence in both public health and healthcare. Although a growing body of literature indicates that CWAs could have positive effects on healthcare, such as improved collaboration, behavioural change, learning, knowledge management, and adaptation of knowledge to local context, this has never been assessed systematically. Moreover, several questions regarding safety, reliability, and legal aspects exist.

OBJECTIVES

The objectives of this review were to (1) assess the effects of the use of CWAs on process (including the behaviour of healthcare professionals) and patient outcomes, (2) critically appraise and summarise current evidence on the use of resources, costs, and cost-effectiveness associated with CWAs to improve professional practices and patient outcomes, and (3) explore the effects of different CWA features (e.g. open versus closed) and different implementation factors (e.g. the presence of a moderator) on process and patient outcomes.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, and 11 other electronic databases. We searched the grey literature, two trial registries, CWA websites, individual journals, and conference proceedings. We also contacted authors and experts in the field. We did not apply date or language limits. We searched for published literature to August 2016, and grey literature to September 2015.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), non-randomised controlled trials (NRCTs), controlled before-and-after (CBA) studies, interrupted time series (ITS) studies, and repeated measures studies (RMS), in which CWAs were used as an intervention to improve the process of care, patient outcomes, or healthcare costs.

DATA COLLECTION AND ANALYSIS

Teams of two review authors independently assessed the eligibility of studies. Disagreements were resolved by discussion, and when consensus was not reached, a third review author was consulted.

MAIN RESULTS

We screened 11,993 studies identified from the electronic database searches and 346 studies from grey literature sources. We analysed the full text of 99 studies. None of the studies met the eligibility criteria; two potentially relevant studies are ongoing.

AUTHORS' CONCLUSIONS

While there is a high number of published studies about CWAs, indicating that this is an active field of research, additional studies using rigorous experimental designs are needed to assess their impact and cost-effectiveness on process and patient outcomes.