Big Data and Analytics for Healthcare

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

Deadline for manuscript submissions: closed (15 February 2016) | Viewed by 8778

Special Issue Editor


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Guest Editor
Strategic Innovation Group, Booz Allen Hamilton, 308 Sentinel Drive, Suite 100, Annapolis Junction, MD 20701, USA
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

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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

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Published Papers (1 paper)

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2922 KiB  
Article
Enabling Virtual Sensing as a Service
by Yang Li, Ioannis Pandis and Yike Guo
Informatics 2016, 3(2), 3; https://doi.org/10.3390/informatics3020003 - 29 Mar 2016
Cited by 9 | Viewed by 8455
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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