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Types of PGMs

Types of PGMs

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Figure 1: Structure of Bayesian network. Figure adopted from 1
Figure 2: Types of PGMs
Figure 3: Bayesian networks over three variables, encoding different...
Figure 4: Bayesian networks: Directed graphical models
+9 Figure 5: Bayesian network with conditional probability distributions...
Decision Support Systems for Healthcare based on Probabilistic Graphical Models: A Survey and Perspective
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  • Nov 2021
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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... there are two representations commonly used, (1) Bayesian networks and (2) Markov random fields, as shown in Figure 2. Depending on whether the graph is directed or undirected, we can classify graphical models into Bayesian networks and Markov networks, respectively. ...

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Enhancing Healthcare Decision Support Systems With Advanced Analytics and Machine Learning Techniques
Chapter
  • Jul 2024
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.
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AUTAN-ECG: An AUToencoder bAsed system for anomaly detectioN in ECG signals
Preprint
Full-text available
  • Dec 2023
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 .
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