Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice. We aimed to develop a deep-learning framework for accurate and yet explainable prediction of 6-month incident heart failure (HF). Using 100,071 patients from longitudinal linked electronic health records across the UK, we applied a novel Transformer-based risk model using all community and hospital diagnoses and medications contextualized within the age and calendar year for each patient's clinical encounter. Feature importance was investigated with an ablation analysis to compare model performance when alternatively removing features and by comparing the variability of temporal representations. A post-hoc perturbation technique was conducted to propagate the changes in the input to the outcome for feature contribution analyses. Our model achieved 0.93 area under the receiver operator curve and 0.69 area under the precision-recall curve on internal 5-fold cross validation and outperformed existing deep learning models. Ablation analysis indicated medication is important for predicting HF risk, calendar year is more important than chronological age, which was further reinforced by temporal variability analysis. Contribution analyses identified risk factors that are closely related to HF. Many of them were consistent with existing knowledge from clinical and epidemiological research but several new associations were revealed which had not been considered in expert-driven risk prediction models. In conclusion, the results highlight that our deep learning model, in addition high predictive performance, can inform data-driven risk factor identification.
Traditional heart failure markers fail to reliably predict heart-failure related hospitalisations and deaths. Multisensor patch data can provide an objective insight into activity and sleep patterns of patients and may therefore improve the performance of current risk-quantification algorithms. This work aimed to establish the feasibility of collecting multi-sensor patch data from heart failure patients and to perform an initial analysis of activity and sleep patterns of heart failure patients in relation to disease severity. 13 heart failure patients from the SUPPORT-HF study were provided with chest-worn multisensor patches and asked to wear the devices continuously for up to seven consecutive days. Using a combination of impedance, heart rate and accelerometer data participants' sleep and wakefulness information were extracted and analyzed in relation to self-reported symptom scores. Patch data for eleven patients were of high enough quality to be included in the analysis, accounting for 63 patient days worth of data. The heart failure patients slept for an average of 8.3 hours a night and experienced 2.8 sleep interruptions. Potential differences in sleep angle, heart rate and wake-time activity were found for patients with different heart failure severity. Larger studies are necessary to create a more coherent picture of the potential of activity and sleep as a markers for heart failure deterioration.
Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning. In this work, we propose a distance complementary to existing measures: Topology Distance (TD), the main idea behind which is to compare the geometric and topological features of the latent manifold of real data with those of generated data. More specifically, we build Vietoris-Rips complex on image features, and define TD based on the differences in persistent-homology groups of the two manifolds. We compare TD with the most commonly-used and relevant measures in the field, including Inception Score (IS), Fr\'echet Inception Distance (FID), Kernel Inception Distance (KID) and Geometry Score (GS), in a range of experiments on various datasets. We demonstrate the unique advantage and superiority of our proposed approach over the aforementioned metrics. A combination of our empirical results and the theoretical argument we propose in favour of TD, strongly supports the claim that TD is a powerful candidate metric that researchers can employ when aiming to automatically evaluate the goodness of GANs’ learning.
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