http://iet.metastore.ingenta.com
1887

Decision support system to detect hidden pathologies of stroke: the CIPHER project

Decision support system to detect hidden pathologies of stroke: the CIPHER project

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Big Data Recommender Systems - Volume 2: Application Paradigms — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Currently, it is difficult to find platforms connected to health systems that exploit data in a coherent way and that allow, on the one hand, to send sanitary warnings and on the other, to validate the performance of medical specialists according to the models set by the best practices of the specialty. This chapter aims to explain the CIPHER project, a decision support system (DSS), based on machine-learning (ML) and big data technologies, capable of alerting a clinician when a situation of risk is detected in a patient suffering from a certain pathology, so that could be able to carry out the appropriate measures. CIPHER, is a project born from scratch. For its development, different methodologies, such as design sprint (for product prototyping), navigational development techniques (for product analysis and testing) or SCRUM (for product development), have been applied. In addition, this product has been defined in direct contact with medical specialists and under the umbrella of international standards and models such as ISO 13606, SNOMED, REGICOR or CHADS2. As a result of the development of this product, we have obtained a DSS, which offers health professionals the possibility of receiving alerts from patients who may be at risk of suffering from a specific pathology, based on a series of criteria defined by international standards. Moreover, health professionals would be able to find hidden symptomatology of the pathology mentioned above, which, a priori, are not known.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 Context: the CIPHER project
  • 7.3 Decision support system
  • 7.4 Validation
  • 7.4.1 Data processing
  • 7.4.2 Algorithm selection
  • 7.4.3 First results
  • 7.5 Conclusions and future works
  • Acknowledgments
  • References

Inspec keywords: learning (artificial intelligence); medical information systems; decision making

Other keywords: DSS; CIPHER project; scrum; machine-learning; health systems; product analysis; product development; ML; Big Data technologies; product testing; pathology; decision support system

Subjects: Biology and medical computing; Neural computing techniques; Knowledge engineering techniques

Preview this chapter:
Zoom in
Zoomout

Decision support system to detect hidden pathologies of stroke: the CIPHER project, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch7-1.gif /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch7-2.gif

Related content

content/books/10.1049/pbpc035g_ch7
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address