Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm

@article{Zhong2017EnhancingHR,
  title={Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm},
  author={Hongye Zhong and Jitian Xiao},
  journal={Sci. Program.},
  year={2017},
  volume={2017},
  pages={1901876:1-1901876:18}
}
With recent advances in health systems, the amount of health data is expanding rapidly in various formats. [] Key Method Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as…

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References

SHOWING 1-10 OF 66 REFERENCES

Deep learning for healthcare decision making with EMRs

TLDR
The experimental results indicate that the proposed deep model is able to reveal previously unknown concepts and performs much better than the conventional shallow models.

A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare

TLDR
This paper proposes a generic semantic big data architecture based on the “Knowledge as a Service” approach to cope with heterogeneity and scalability challenges, and focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data.

Online Learning towards Big Data Analysis in Health Informatics

TLDR
This paper introduces online learning and proposes the method for data mining of big data in health informatics, where online learning will preform the data analysis dynamically by the time the data are generated.

Big Data in the Health Sector

TLDR
The trend towards value-based healthcare delivery will foster the collaboration of the stakeholder to enhance the value of the patient’s treatment, and thus will significantly foster the need for big data applications, resulting in a big data revolution in the healthcare domain.

Beyond a Technical Perspective: Understanding Big Data Capabilities in Health Care

TLDR
This study examines the development, architecture and component functionalities of big data, and identifies its capabilities, including traceability, the analysis of unstructured data and patterns of care, and its predictive capacity to support healthcare managers seeking to formulate more effective big-data-based strategies.

Data Mining in Healthcare – A Review

Big Data as an e-Health Service

  • W. LiuE. K. Park
  • Computer Science, Medicine
    2014 International Conference on Computing, Networking and Communications (ICNC)
  • 2014
TLDR
This paper explains why the existing Big Data technologies such as Hadoop, MapReduce, STORM and the like cannot be simply applied to e-Health services directly, and describes the additional capabilities as required in order to make Big Data services for e- health become practical.

Application of the Naïve Bayesian Classifier to optimize treatment decisions.

  • J. KazmierskaJ. Malicki
  • Medicine
    Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
  • 2008
...