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The main purpose of a healthcare support system is to provide %timely and accurate information to clinicians, patients, and others to inform decisions about healthcare. Healthcare support systems can potentially lower costs, improve efficiency, and reduce patient inconvenience. For example, Healthcare support systems can help by alerting clinicians...
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... model was able to distinguish patients with pneumonia from patients with other diseases with high sensitivity (0.95) and specificity (0.965), and was used for many years in the clinic. Figure 1 outline the network proposed in 1 . Regarding the application of PGMs in healthcare, probabilistic methods lie primarily in the realm of Artificial Intelligence (AI). ...
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... are a type of generative deep learning method which learns latent representations. Fig- ure 12 shows a typical structure of a VAE. VAEs have also been used to draw images, achieve state-of-the-art results in semi-supervised learning for healthcare. ...
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... VAEs parametric distribution can be achieved. Hence, during the run time, we can construct new samples from the normal distribution and feed them to the encoder function to generate samples, as depicted in Figure 14. The main difference between traditional autoencoder and variational autoencoders is that the former has no continuous latent space, while the latter has continuous latent space (a sample is mapped to a probability distribution with a certain mean and variance). ...
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... main difference between traditional autoencoder and variational autoencoders is that the former has no continuous latent space, while the latter has continuous latent space (a sample is mapped to a probability distribution with a certain mean and variance). Figure 13 depicts a comparison between the mapping of input data to latent space by an autoencoder and a variational encoder. The main objective of this case study is to use the VAE to learn the latent representations of the data. ...
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... helps the decoder to map from every area of the latent space when decoding the input ECG signal. Figure 14 shows a graphical representation of the VAE for new sample generation. We used the public baseline ECG dataset 86 to train and test our VAE for anomaly detection. ...
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... used the public baseline ECG dataset 86 to train and test our VAE for anomaly detection. Figure 15 shows a scatter plot of the latent space generated by the encoder for the test dataset, after training for 50 epochs, with SGD optimization. The color of each point reflects its associated reconstruction error. ...
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... performance can be enhanced by optimizing the hyper-parameters and adjusting the layer structure of the VAE. Figure 15: Anomaly detection using VAE VAEs are widely used for a variety of machine learning tasks. This case study was a practical example providing a simple example that can be used to prototype and test it in healthcare application. ...
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... the region with cause the anomalous behaviour can be trace out. The example shown in Figure 16 was introduced in 51 to trace back the regions which are responsible for predicting a particular class. The regions in red are responsible for classification of the ECG signal in to the output class (color weighted scale shows contribution of each region). ...
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... approaches can be adopted in anomaly detection to trace and explain the regions which cause the anomalous behaviour. Figure 16: Explanation of the regions responsible for a particular class To summarize, traditional PGMS work well with discrete variables. Nevertheless, the neural network based PGMs enhance their abilities to continuous high-dimensional data. ...
Citations
This chapter outlines the need for intelligent decision support, growing complexity of healthcare systems, key concepts of advanced analytics, and elucidating techniques such as data preprocessing and feature engineering. The survey extends to address challenges and opportunities within the realm of healthcare analytics, offering insights into ethical considerations, privacy concerns, and regulatory implications. Real-world case studies serve to illuminate successful implementations and extract valuable lessons, fostering a deeper understanding of practical applications. This chapter explores the integration of analytics with electronic health records (EHR), examining strategies to enhance decision support through the utilization of comprehensive healthcare data. The chapter, by distilling pertinent information from myriad sources, aims to provide a valuable resource for researchers, practitioners, and policymakers navigating the dynamic intersection of advanced analytics, machine learning, and healthcare decision support systems.
Electrocardiographic (ECG) signals that monitor heart activity can help identifying disease-related anomalies. Reliable automatic anomaly detection has been shown to be useful in supporting physicians in reading ECG signals. Decision support systems may be useful in such cases but their reliability can be guaranteed.
Autoencoders (AEs) have been extensively used to analyse signals in many fields. Convolutional Autoencoders (CAE) are a particular class of AE showing optimal performances in detecting signal anomalies. Thus, CAEs can be used to support and automatise the task of anomaly detection. We design and use a CAE-based system to detect anomalies in ECG signals to support cardiologists in identifying anomalies related to possible diseases. Our tool outperforms other state-of-the-art ECG anomaly detection approaches tested on a real dataset. In the task of anomaly detection, our CAE obtains a ROC AUC of 97.82% with a simulated test set and a ROC AUC of 99.75% using on a real test set. The tool and the source code are available at https://github.com/UgoLomoio/EG_DSS_CAE .
