Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the ‘Deep Early Warning System’ (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45,314 vital-sign measurements as a balanced training set and 359,481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize ‘historical’ trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.

AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. It is challenging to train the effective machine learning models on such data. In this work, we propose dynamic neural graphs based federated learning framework to address these challenges. The proposed framework extends Reptile , a model agnostic meta-learning (MAML) algorithm, to a federated setting. However, unlike the existing MAML algorithms, this paper proposes a dynamic variant of neural graph learning (NGL) to incorporate unlabelled examples in the supervised training setup. Dynamic NGL computes a meta-learning update by performing supervised learning on a labelled training example while performing metric learning on its labelled or unlabelled neighbourhood. This neighbourhood of a labelled example is established dynamically using local graphs built over the batches of training examples. Each local graph is constructed by comparing the similarity between embedding generated by the current state of the model. The introduction of metric learning on the neighbourhood makes this framework semi-supervised in nature. The experimental results on the publicly available MIMIC-III dataset highlight the effectiveness of the proposed framework for both single and multi-task settings under data decentralisation constraints and limited supervision.

Recent advancements in modeling speech and audio signals using deep neural networks have shown that systems learning both features and the classifier can be built directly from raw signal. However, the performance of such end-to-end systems for acoustic scene classification (ASC) task is still not at par with conventional systems built using spectral features. In this work, we propose a raw waveform based end-to-end ASC system using convolutional neural network. In contrast to the existing studies using a non-hierarchical model, our framework leverages the hierarchical relations between acoustic categories to improve the classification performance. To this aim, our multi-task model is trained with coarse and fine labels that correspond to different levels of abstraction. In order to ensure consistency in the encoded information via label hierarchy, the proposed framework uses a prototypical model. Such a model ensures that the learned representations at least match to one of the global categorical learned prototypes. We also employed a statistical pooling layer to aggregate hidden representations over multiple frames of the input audio signal. The statistics (mean and standard deviation) are concatenated together to form a fixed-length audio embedding. This aggregation is done via an attention module so as to guide the model's attention even in presence of relatively short or transient acoustic events. Further, the proposed framework incorporate two parallel feature processing pipelines to achieve different resolutions for extracting important acoustic cues. Various experiments on publicly available datasets are performed to demonstrate the effectiveness of the proposed framework for ASC task. Additional transfer learning experiments showed the proposed model's adaptation capability to unseen data. Network analysis and visualizations demonstrate the importance of individual modules and their impact on overall representation learning for ASC task.

Clinical time-series data retrieved from electronic medical records are widely used to build predictive models of adverse events to support resource management. Such data is often sparse and irregularly-sampled, which makes it challenging to use many common machine learning methods. Missing values may be interpolated by carrying the last value forward, or through linear regression. Gaussian process (GP) regression is also used for performing imputation, and often re-sampling of time-series at regular intervals. The use of GPs can require extensive, and likely adhoc, investigation to determine model structure, such as an appropriate covariance function. This can be challenging for multivariate real-world clinical data, in which time-series variables exhibit different dynamics to one another. In this work, we construct generative models to estimate missing values in clinical time-series data using a neural latent variable model, known as a Neural Process (NP). The NP model employs a conditional prior distribution in the latent space to learn global uncertainty in the data by modelling variations at a local level. In contrast to conventional generative modelling, this prior is not fixed and is itself learned during the training process. Thus, NP model provides the flexibility to adapt to the dynamics of the available clinical data. We propose a variant of the NP framework for efficient modelling of the mutual information between the latent and input spaces, ensuring meaningful learned priors. Experiments using the MIMIC III dataset demonstrate the effectiveness of the proposed approach as compared to conventional methods.

In this paper we propose a conditional generative modelling (CGM) approach for unsupervised disentangled representation learning using variational autoencoder (VAE). CGM employs a multimodal/categorical conditional prior distribution in the latent space to learn global uncertainty in data by modelling the variations at local level. Thus, the proposed framework enforces the model to independently estimate the inherent patterns within each category, which improves the interpretability of the latent representations learned by the VAE model. The evidence lower bound objective for training the generative model is maximized using a mutual information criterion between the global latent categorical code and the encoded inputs. Further, the approach has a built-in mechanism for bounding the information flow between the encoder and the decoder which addresses the problems of posterior collapse in conventional VAE models. Experiments on a variety of datasets demonstrate that our objective can learn disentangled representations and the proposed approach achieves competitive results in various task such as generative modelling, image classification and image denoising.