The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis.

ABSTRACT

Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may exist. This technique has not previously been applied to the risk stratification of patients referred with suspected deep vein thrombosis (DVT). Current assessment is usually with a points-based clinical score, which may be combined with a D-dimer blood test. A neural network was trained to risk-stratify patients presenting with suspected DVT and its performance compared with existing tools. Data from 11 490 cases of suspected DVT presenting consecutively between 1 January 2011 and 31 December 2017 were analysed, and 7080 for whom all components of the Wells' score, a D-dimer and an ultrasound result were available were included in the analysis. The data were broken into a training set of 5270 patients, used to develop the algorithm, and a testing set of 1810 patients to assess performance of the trained algorithm. This network was able to exclude DVT without the need for ultrasound in significantly more patients than existing risk assessment scores, whilst retaining very low false negatives rates. More generally, this approach may improve the analysis of complex data to support decision-making in other areas of clinical medicine.