Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision ...
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Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causalgraphs for each individual is challenging due to the limited amount of data available for each *** In this study, we present a new algorithmic framework using meta-learning for learning personalized causalgraphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causalgraph *** Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causalgraph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline *** To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.
Discovering the causalgraph in multivariate time series data is of great importance for industrial society, yet challenging due to the unknown nonlinearity in the data. Existing works only explore the data in chronol...
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ISBN:
(纸本)9781728146034
Discovering the causalgraph in multivariate time series data is of great importance for industrial society, yet challenging due to the unknown nonlinearity in the data. Existing works only explore the data in chronological order, and rely on pre-assumed kernels or certain distribution assumption. In this paper, we present a Bi-directional neural network for causal graph learning (Bi-CGL) through weight-sharing and low-rank neural network. It discovers the causalgraph by simultaneously exploring input in forward and reverse chronological order. Both directions approach the same causalgraph with shared low-rank approximation, which provides robustness and better accuracy against data noise. Experiments on synthetic and real world datasets prove our Bi-CGL's outperformance over existing baselines.
Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged ...
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ISBN:
(纸本)9798400704901
Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged for traffic prediction and achieved remarkable success. However, these models have limitations in terms of generalization and scalability when dealing with Out-of-Distribution (OOD) graph data with both structural and temporal shifts. To tackle the challenges of spatio-temporal shift, we propose a framework called STONE by learning invariable node dependencies, which achieve stable performance in variable environments. STONE initially employs gated-transformers to extract spatial and temporal semantic graphs. These two kinds of graphs represent spatial and temporal dependencies, respectively. Then we design three techniques to address spatio-temporal shifts. Firstly, we introduce a Frechet embedding method that is insensitive to structural shifts, and this embedding space can integrate loose position dependencies of nodes within the graph. Secondly, we propose a graph intervention mechanism to generate multiple variant environments by perturbing two kinds of semantic graphs without any data augmentations, and STONE can explore invariant node representation from environments. Finally, we further introduce an explore-to-extrapolate risk objective to enhance the variety of generated environments. We conduct experiments on multiple traffic datasets, and the results demonstrate that our proposed model exhibits competitive performance in terms of generalization and scalability.
The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment networks, is evidenced by the growing frequency of breaches and the evolving sophistication of attack methods. Attack detectio...
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ISBN:
(纸本)9798400704369
The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment networks, is evidenced by the growing frequency of breaches and the evolving sophistication of attack methods. Attack detection in such vulnerable critical infrastructures can be generalized into a task of anomaly detection with multivariate stream data. There are two essential challenges of this task: 1) Evolving and Shifting data streams;and 2) Robust Attack Pattern representation. Existing anomaly detection approaches, including statistical, distance, density, neural network, and graph-based methods, are not specialized in solving the spurious statistical relationships of evolving distribution shifts in sensing data streams. To address the two challenges, we propose a multi-view causalgraph perspective, where 1) We build causalgraphs to capture invariant anomaly patterns in varying streams;and 2) Introduce multi-view fusion for robust attack pattern representation. To implement this technical perspective, we develop a fused multi-view causalgraph-aware anomaly detection framework. This framework includes two phases: 1) Multi-view causalgraphs and Spectral Fusion, where we learn the dense view and sparse view causalgraphs from sensory data streams and fuse the two causalgraphs into a single weighted Laplacian matrix representation. 2) graph Anomaly Detection, where we train a Deep Convolutional graph Neural Network (DGCNN) on the Laplacian representation of the "Attack" and "Normal" status graphs to detect attack statuses on sensory data streams per time interval. Our framework achieves a ROC-Score of 82.4% and 93.2% on the SWaT andWADIWater Treatment Network Datasets with an improvement of 9.03% and 16.5% on the f1-score respectively when compared with the best-performing baseline methods on both the datasets. The proposed method, related code, and the tested datasets are available in the following git repository https://***/arunvignesh28/SMV- CGAD.
Objective: We aimed to learn the causal determinants of postoperative length of stay in cardiac surgery patients undergoing isolated coronary artery bypass grafting or aortic valve replacement surgery. Methods: For pa...
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Objective: We aimed to learn the causal determinants of postoperative length of stay in cardiac surgery patients undergoing isolated coronary artery bypass grafting or aortic valve replacement surgery. Methods: For patients undergoing isolated coronary artery bypass grafting or isolated aortic valve replacement surgeries between 2011 and 2016, we used causalgraphical modeling on electronic health record data. The Fast causal Inference (FCI) algorithm from the Tetrad software was used on data to estimate a Partial Ancestral graph (PAG) depicting direct and indirect causes of postoperative length of stay, given background clinical knowledge. Then, we used the latent variable intervention-calculus when the directed acyclic graph is absent (Ly-IDA) algorithm to estimate strengths of causal effects of interest. Finally, we ran a linear regression for postoperative length of stay to contrast statistical associations with what was learned by our causal analysis. Results: In our cohort of 2610 patients, the mean postoperative length of stay was 219 hours compared with the Society of Thoracic Surgeons 2016 national mean postoperative length of stay of approximately 168 hours. Most variables that clinicians believe to be predictors of postoperative length of stay were found to be causes, but some were direct (eg, age, diabetes, hematocrit, total operating time, and postoperative complications), and others were indirect (including gender, race, and operating surgeon). The strongest average causal effects on postoperative length of stay were exhibited by preoperative dialysis (209 hours);neuro-, pulmonary-, and infection-related postoperative complications (315 hours, 89 hours, and 131 hours, respectively);reintubation (61 hours);extubation in operating room (-47 hours);and total operating room duration (48 hours). Linear regression coefficients diverged from causal effects in magnitude (eg, dialysis) and direction (eg, crossclamp time). Conclusions: By using retrospective ele
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