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Clinical Assessment for Deep Vein Thrombosis using Support Vector Machines: A description of a clinical assessment and compression ultrasonography journaling system for deep vein thrombosis using support vector machines
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesisAlternative title
Klinisk bedömning av djup ventrombos genom SVMs (Swedish)
Abstract [en]

This master thesis describes a journaling system for compression ultrasonography and a clinical assessment system for deep vein thrombosis (DVT). We evaluate Support Vector Machines (SVM) models with linear- and radial basis function-kernels for predicting deep vein thrombosis, and for facilitating creation of new clinical DVT assessment.

Data from 159 patients where analysed, with our dataset, Wells Score with a high clinical probability have an accuracy of 58%, sensitivity 60% and specificity of 57% these figured should be compared to those of our base models accuracy of 81%, sensitivity 66% and specificity 84%. A 23 percentage point increase in accuracy.The diagnostic odds ratio went from 2.12 to 11.26. However a larger dataset is required to report anything conclusive.

As our system is both a journaling and prediction system, every patient examined helps the accuracy of the assessment.

Abstract [sv]

I denna rapport beskrivs ett journalsystem samt ett system för klinisk bedömning av djupvenstromboser.Vår modell baserar sig på en stödvektormaskin (eng. Support Vector Machine) med linjär och radial basfunktion för att fastställa förekomsten av djupa ventromboser samt att hjälpa till i skapandet av nya modeller för bedömning.

159 patientjournaler användes för att fastställa att Wells Score har en klinisk precision på 58%, 60% sensitivitet och specificitet på 57% somkan jämföras med våran modell som har en precision på 81%, 66% sensitivitet och specificitet på 84%. En 23 procentenheters ökning i precision.Den diagnostiska oddskvoten gick från 2.12 till 11.26. Det behövs dock en större datamängd för att rapportera något avgörande.

Då vårt system både är för journalskapande och klinisk bedömning så kommer varje undersökt patient att bidra till högre precision i modellen.

Place, publisher, year, edition, pages
2015.
Keyword [en]
clinical assessment, compression ultrasonography, deep vein thrombosis, support vector machines
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-178419OAI: oai:DiVA.org:kth-178419DiVA: diva2:877778
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Supervisors
Examiners
Available from: 2015-12-08 Created: 2015-12-07 Last updated: 2015-12-08Bibliographically approved

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