Limitations and future work
breathlessness
EVENTS
The major limitation of this study is the lack validation of the model in an external dataset. While some studies hold out a proportion of the original data to create an external validation dataset, this technique was not possible here due to restrictions of sample size. To address these limitations, we used a cross validation approach to provide an indication of out of sample transferability in which the support vector machine was exposed to multiple iterations of the sub-sampled dataset during model training, and therefore never ‘saw’ the entire dataset until the test phase. Models with a large number of measures compared with events (responder or non-responder) risk overfitting and demonstrate poor generalisability to novel datasets. Our dataset contained 35 potential features and therefore was at risk of overfitting. To address this issue, we reduced the number of data-dimensions via feature selection, employed cross-validation and used an automated tuning of the regularisation parameter ‘C’. However, while these techniques may ameliorate some of the risk of overfitting, a future study with larger sample size, or independently collected datasets, would take the next steps to externally validate the brain-behaviour model and allow assessment of generalisability.A key feature of support vector machines is that they fit high-dimensional discriminatory planes between multiple measures to predict an outcome. This multivariate approach affords greater sensitivity in distinguishing between non-separable distributions along a single dimension. The additional use of a non-linear kernel also enables us to capture relationships between highly disparate biological features which often demonstrate non-linear profiles and as a result predict changes in breathlessness. Although this technique leads to less intuitive interpretations of feature weightings, the methods used are the first to successfully demonstrate a relationship between breathlessness expectation related brain activity and changes to reported breathlessness over pulmonary rehabilitation. To reconcile these challenges and move towards eventual clinical application, we suggest that this model form the basis for further studies scrutiny via first an external validation dataset and then further interventional studies.While larger sample sizes are now required to translate these mechanistic models into clinical relevance, the data provides evidence that breathlessness expectation related brain activity at baseline strongly influences how patients respond to treatment in a predictable manner.