Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
The role of Machine Learning in Predicting CABG Surgery Duration
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
2011 (engelsk)Independent thesis Advanced level (degree of Master (Two Years))Oppgave
Abstract [en]

Context. Operating room (OR) is one of the most expensive resources of a hospital. Its mismanagement is associated with high costs and revenues. There are various factors which may cause OR mismanagement, one of them is wrong estimation of surgery duration. The surgeons underestimate or overestimate surgery duration which causes underutilization or overutilization of OR and medical staff. Resolving the issue of wrong estimate can result improvement of the overall OR planning. Objectives. In this study we investigate two different techniques of feature selection, compare different regression based modeling techniques for surgery duration prediction. One of these techniques (with lowest mean absolute) is used for building a model. We further propose a framework for implementation of this model in the real world setup. Results. In our case the selected technique (correlation based feature selection with best first search in backward direction) for feature selection could not produce better results than the expert’s opinion based approach for feature selection. Linear regression outperformed on both the data sets. Comparatively the mean absolute error of linear regression on experts’ opinion based data set was the lowest. Conclusions. We have concluded that patterns exist for the relationship of the resultant prediction (surgery duration) and other important features related to patient characteristics. Thus, machine learning tools can be used for predicting surgery duration. We have also concluded that the proposed framework may be used as a decision support tool for facilitation in surgery duration prediction which can improve the planning of ORs and their resources.

sted, utgiver, år, opplag, sider
2011. , s. 56
Emneord [en]
Machine learning, surgery duration prediction, operating room planning, data mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-6074Lokal ID: oai:bth.se:arkivex0A61803AA6E79117C125792E003527A6OAI: oai:DiVA.org:bth-6074DiVA, id: diva2:833493
Uppsök
Technology
Veileder
Merknad
Zahoor Ali 00923339474002 Muhammad Qummer ul Arfeen 0046760652203Tilgjengelig fra: 2015-04-22 Laget: 2011-10-19 Sist oppdatert: 2018-01-11bibliografisk kontrollert

Open Access i DiVA

fulltekst(1338 kB)461 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1338 kBChecksum SHA-512
6ae4c931c116ba70fcc6b7bd2a2613cd6477ab369a2e3c855210eabe94a0c876581083cc2905bf2e7c024bc45539cbdc60072e493211bee5df6760016472ae93
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 461 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 589 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf