Comprehensive electrocardiographic diagnosis based on deep learning

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals

Deep learning for electronic health records: risk prediction, explainability, and uncertainty

Background: Risk models are essential for care planning and disease prevention. The unsatisfactory performance of the established clinical models has raised broad awareness and concerns. An accurate, explainable, and reliable risk model is highly beneficial but remains a challenge. Objective: This thesis aims to develop deep learning models that can make more accurate risk predictions with the provision of uncertainty estimation and the ability to provide medical explanations using a large and representative electronic health records (EHR) dataset. Methods: We investigated three directions in this thesis: risk prediction, explainability, and uncertainty estimation. For risk prediction, we investigated deep learning tools that can incorporate the minimal processed EHR for modelling and comprehensively compared them with the established machine learning and clinical models. Additionally, the post-hoc explanations were applied to deep learning models for medical information retrieval, and we specifically looked into explanations in risk association and counterfactual reasoning. Uncertainty estimation was qualitatively investigated using probabilistic modelling techniques. Our analyses relied on Clinical Practice Research Datalink, which contains anonymised EHR collected from primary care, secondary care, and death registration and is representative of the UK population. Results: We introduced a deep learning model, named BEHRT, that can incorporate minimal processed EHR for risk prediction. Without expert engagement, it learned meaningful representations that can automatically cluster highly correlated diseases. Compared to the established machine learning and clinical models that relied on expert- selected predictors, our proposed deep learning model showed superior performance on a wide range of risk prediction tasks and highlighted the necessity of recalibration when applying a risk model to a population with severe prior distribution shifts, and the importance of regular model updating to preserve the model’s discrimination performance under temporal data shifts. Additionally, we showed that the deep learning model explanation is an excellent tool for discovering risk factors. By explaining the deep learning model, we not only identified factors that were highly consistent with the established evidence but also those that have not been considered in expert-driven studies. Furthermore, the deep learning model also captured the interplay between risk and treated risk and the differential association of medications across different years, which would be difficult if the temporal context was not included in the modelling. Besides the explanations in terms of association, we introduced a framework that can achieve accurate risk prediction, while enabling counterfactual reasoning under hypothetical interventions. This offers counterfactual explanations that could inform clinicians for selection of those who will benefit the most. We demonstrated the benefit of the proposed framework using two exemplary case studies. Furthermore, transforming a deterministic deep learning model to probabilistic can make predictions with an uncertainty range. We showed that such information has many potential implications in practice, such as quantifying the confidence of a decision, indicating data insufficiency, distinguishing the correct and incorrect predictions, and indicating risk associations. Conclusions: Deep learning models led to substantially improved performance for risk prediction. The ability of uncertainty estimation can quantify the confidence of risk prediction to further inform clinical decision-making. Deep learning model explanation can generate hypotheses to guide medical research and provide counterfactual analysis to assist clinical decision-making. This encouraging evidence supports the great potential of incorporating deep learning methods into electronic health records to inform a wide range of health applications such as care planning, disease prevention, and medical study design