Special Issue "Big Data and Analytics for Healthcare"

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (15 February 2016)

Special Issue Editor

Guest Editor
Dr. Kirk D. Borne

Strategic Innovation Group, Booz Allen Hamilton, 308 Sentinel Drive, Suite 100, Annapolis Junction, MD 20701, USA
Website | E-Mail
Interests: data science; outlier detection; data mining; machine learning; artificial intelligence; discovery science; big data; statistics; information systems; databases; semantics; ontologies; annotation; indexing; recommender systems; space science informatics; education informatics; x-informatics; computational informatics; social informatics; health informatics

Special Issue Information

Dear Colleagues,

The growth of data in bio-medical, pharma, and health systems as well as the growing awareness of the power of analytics across public and private sector health organizations are signs that healthcare is one of the fastest-growing application areas for big data. Healthcare data systems include electronic health records, data-sharing exchanges, clinical trial results, and medical research studies, coding standards for terminology (including for treatments, causes, symptoms, and diagnoses), insurance claims, hospital admission systems, surveillance systems, and much more. The application of analytics for discovery, decision support, and benefits in healthcare has the potential to radically improve health maintenance, treatments, diagnoses, and outcomes in personalized medicine, personal health (e.g., wearable devices), and population health. Predictive analytics enables better forecasting, treatments, risk mitigation, compliance, and fraud detection. Prescriptive analytics offers an opportunity to deliver more efficient and effective treatment and health outcomes. This is especially important as longer life spans and the aging of the population place greater stresses and requirements on health systems. Wearable devices are just one example from the broader exploding big data analytics opportunity that the Internet of Things brings to healthcare, where sensors and devices are monitoring almost everything (persons, places, things, systems, and processes). This Special Issue of the journal Informatics will address the rich diversity of uses and applications of big data and analytics in healthcare. Research papers that focus on empirical studies and evaluations as well as conceptual and position papers will be considered for publication in this Special Issue.

Dr. Kirk D. Borne
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Clinical Informatics
  • Machine Learning
  • Data Mining
  • Data Science
  • Predictive Analytics
  • Insurance Fraud
  • Risk Analytics
  • Modeling
  • EHR (Electronic Health Record)
  • EMR (Electronic Medical Record)
  • ICD10
  • HIE (Health Information Exchange)
  • Data Sharing
  • Drug Discovery
  • Ontologies

Published Papers (1 paper)

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Research

Open AccessArticle Enabling Virtual Sensing as a Service
Informatics 2016, 3(2), 3; doi:10.3390/informatics3020003
Received: 12 January 2016 / Revised: 9 March 2016 / Accepted: 22 March 2016 / Published: 29 March 2016
Cited by 1 | PDF Full-text (2922 KB) | HTML Full-text | XML Full-text
Abstract
In many situations, placing a physical sensor in the ideal position in or on the human body to acquire sensing data is incredibly difficult. Virtual sensors, in contrast to physical sensors, can provide indirect measurements by making use of other available sensor data.
[...] Read more.
In many situations, placing a physical sensor in the ideal position in or on the human body to acquire sensing data is incredibly difficult. Virtual sensors, in contrast to physical sensors, can provide indirect measurements by making use of other available sensor data. In this paper, we demonstrate a virtual sensing application developed as a service on top of a cloud-based health sensor data management platform called Wiki-Health. The proposed application “implants” virtual sensors in the human body by integrating environmental, geographic and personal sensor data with physiological models to compute temperature estimations of various parts of the body. The feasibility of the proposed virtual sensing service is supported by a case study. The ability to share computational models relevant to do calculations on measured data on the go is also discussed. Full article
(This article belongs to the Special Issue Big Data and Analytics for Healthcare)
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