Digitala Vetenskapliga Arkivet

Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identification of Surgery Indicators by Mining Hospital Data: A Preliminary Study
Blekinge Institute of Technology, School of Computing, Ronneby, Sweden.ORCID iD: 0000-0002-0535-1761
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The management of patient referrals is an interesting issue when it comes to predicting future patient demand to increase hospital productivity. In general, a patient is referred from the general practitioner to hospital care. A patient referral contains information that indicates the need for hospital care and this information is differently structured for different medical needs. In practice, these needs can be viewed as the forthcoming patient demand at the hospital, analogous to a volume of orders. Today, the structure of the referrals is very much up to the general practitioner who is referring the patient. This implies that the data provided to the hospital can vary extensively between cases. We suggest that, by enforcing a certain structure on the referral data, it may be possible to make early predictions about the patient demand. Such predictions could then be used as a basis for managing resources more efficiently to increase hospital productivity. This paper investigates the possibility of using data mining techniques to automatically generate prediction models by extracting conclusive information from patient records combined with surgical suite statistics, ,e.g., surgery preparations and anesthesia type, that are of significance for estimating patient demand in a surgery department, e.g., probability of surgery, surgery duration and recovery. We hypothesize that the generated models may provide new knowledge about, and a basis for, how to structure a patient referral. In addition, these models may also be used for the actual prediction of patient demand.

Place, publisher, year, edition, pages
Linz, Austria: IEEE Press, 2009.
Keywords [en]
surgery, indicators, data mining, supervised learning, healthcare, hospital
National Category
Computer Sciences Medical and Health Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37961DOI: 10.1109/DEXA.2009.69ISI: 000275655300061ISBN: 978-0-7695-3763-4 (print)OAI: oai:DiVA.org:hj-37961DiVA, id: diva2:1159729
Conference
20th International Workshop on Database and Expert Systems Applications
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(187 kB)156 downloads
File information
File name FULLTEXT01.pdfFile size 187 kBChecksum SHA-512
6b6f48d997a8f0a4efd9e4e115c3f5098806c541d1b0296a7e375c6d0640ffccdb9747fe5c987d9e5f288b1d09ee9b48f86f185a0dbcbd5cae8081e59e42e639
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Lavesson, Niklas
Computer SciencesMedical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 156 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 147 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf