This project 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 was 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.
Source: KTH
Author: Daniel, Öberg