Identifying significant covariates for anti-HIV treatment response: mechanism-based differential equation models and empirical semiparametric regression models

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data

ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models-those using difference maps from known reference points-outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it.

Predictors of H1N1 vaccination in pregnancy

The purpose of this review was to determine factors that influence a pregnant woman's acceptance of the H1N1 vaccine with the use of the Health Belief Model (HBM). A self-administered questionnaire based on the HBM was used in a cross-sectional study of postpartum women during the 2009 H1N1 epidemic. Overall, 212 postpartum women were approached and agreed to participate; of these women, 25.5% had received an H1N1 vaccination. Perceived barriers to vaccination (P = .001) and perceived severity of infection (P = .018) were independent predictors of vaccination. The total predictive utility of the full model that incorporated HBM dimensions, age, race, care provider, and education level was moderate (area under the curve, -0.86). The addressing of perceived barriers (such as fear of side-effects), an explanation of the safety of the vaccine for the fetus, and the stressing of complications that are associated with H1N1 infection in pregnancy may increase the rate of vaccination.