Correlation Graph Research Articles

Comparative efficacy and acceptability of different exercise patterns for reducing cardiovascular events in pre-diabetes: protocol for a systematic review and network meta-analysis of randomised controlled trials

IntroductionExercise has been used to reverse dysglycaemic states in patients with pre-diabetes. Systematic reviews show that exercise is an effective way to reduce the incidence of diabetes, but there is...

Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations

Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order relations between nodes. However, recent studies show that social recommendations still face the following challenges: 1) a majority of social recommendations ignore the impact of multifaceted social relationships on user preferences; 2) the item homogeneity is often neglected, mainly referring to items with similar static attributes have similar attractiveness when exposed to users that indicating hidden links between items; and 3) directly combining the representations learned from different independent views cannot fully exploit the potential connections between different views. To address these challenges, in this article, we propose a novel method DH-HGCN++ for multiple social recommendations. Specifically, dual homogeneity (i.e., social homogeneity and item homogeneity) is introduced to mine the impact of diverse social relations on user preferences and enrich item representations. Hypergraph convolution networks with motifs are further exploited to model the high-order relations between nodes. Finally, cross-view contrastive learning is proposed as an auxiliary task to jointly optimize the DH-HGCN++. Real-world datasets are used to validate the effectiveness of the proposed model, where we use sentiment analysis to extract comment relations and employ the k-means clustering algorithm to construct the item-item correlation graph. Experiment results demonstrate that our proposed method consistently outperforms the state-of-the-art baselines on Top-N recommendations.

A Metro Passenger Flow Forecasting Model Based On Time-series Evolving Interaction Graph Network

Passenger flow forecasting is an important task in metro operation management. In order to achieve more accurate metro passenger flow forecasting, this paper proposes a metro passenger flow forecasting model based on time-series evolution interaction graph. First, by introducing two kinds of inter-station interaction graphs, namely connectivity graph and temporal correlation graph, to capture the potential interaction relationship among metro passenger flow stations. Then, by using the time-series evolving graph, the weights of the graph convolutional neural network are dynamically evolved in time series. Finally, taking Suzhou Metro as an example, the short-term passenger flow of the metro is forecasted. The experimental results show that the Root Mean Squared Error (RMSE) of this model is 34.17, the Mean Absolute Error (MAE) is 16.35, the R-Squared is 0.94, the Mean Absolute Percent Error (MAPE) is 0.21. All evaluation metrics are better than the baseline models, thus verifying the effectiveness and applicability of the metro passenger flow forecasting model based on the time series evolution interaction graph.

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

In the dynamic global trade environment, accurately predicting trade values of diverse commodities is challenged by unpredictable economic and political changes. This study introduces the Meta-TFSTL framework, an innovative neural model that integrates Meta-Learning Enhanced Trade Forecasting with efficient multicommodity STL decomposition to adeptly navigate the complexities of forecasting. Our approach begins with STL decomposition to partition trade value sequences into seasonal, trend, and residual elements, identifying a potential 10-month economic cycle through the Ljung–Box test. The model employs a dual-channel spatiotemporal encoder for processing these components, ensuring a comprehensive grasp of temporal correlations. By constructing spatial and temporal graphs leveraging correlation matrices and graph embeddings and introducing fused attention and multitasking strategies at the decoding phase, Meta-TFSTL surpasses benchmark models in performance. Additionally, integrating meta-learning and fine-tuning techniques enhances shared knowledge across import and export trade predictions. Ultimately, our research significantly advances the precision and efficiency of trade forecasting in a volatile global economic scenario.

Popularity Bias in Correlation Graph based API Recommendation for Mashup Creation

The explosive growth of the API economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the p opularity b ias aware w eb A PI r ecommendation ( PB-WAR ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real world dataset demonstrate that PB-WAR offers the optimal trade-off between accuracy and debiasing performance compared to other competitive methods.

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.

The search for new pathogenesis of cardiorenal syndrome: the effect of local Schumann resonance on the occurrence of episodes of kidney disease and myocardial infarction

Background. The pandemic of noncommunicable chronic diseases and the high prevalence of combined damage to the cardiovascular system and kidneys determine the relevance of continuing scientific research to solve these medical problems. Therefore, the aim of this study was to compare the influence of the Earth’s electromagnetic field on the occurrence of episodes of kidney disease and myocardial infarction in order to search for new pathogenetic components of cardiorenal syndrome and deepen fundamental knowledge. According to the Lithuanian magnetometer GCI003, a number of stu­dies in 2014–2018 found that changes in the Earth’s electromagnetic field may play an important role in the pathogenesis of cardiovascular diseases as well as their incidence. Since the functioning of the cardiovascular system and kidneys are closely connected through the metabolic processes of the cardiorenal metabolic axis, this study tested the hypothesis that changes in the Earth’s electromagnetic field may also affect the pathogenesis of kidney disease as the changes of local magnetic field have been shown to influence the functioning of the cardiovascular system. Materials and methods. This was a search retrospective study on the relationship between the influence of local Schumann resonances and the occurrence of hospitalizations in 1340 patients with kidney disease. It also examined the relationship between local Schumann resonances and heart attacks in patients admitted to the University Hospital of the Lithuanian University of Health Sciences (703 patients). Mean power of local magnetic field fluctuations in Lithuania was measured in pT2 s2 in five different frequency ranges, which overlaps the Schumann resonance and electroencephalogram’s frequency ranges: SDelta (0–3.5 Hz), STheta (3.5–7 Hz), SAlpha (7–15 Hz), SBeta (15–32 Hz), SGamma (32–66 Hz). The data of hospitalizations to the Nephrology Department of University Hospital and the dynamics of Schumann resonances were analyzed from January 1, 2021 to December 31, 2021. The data of hospitalizations for myocardial infarction to the Cardiology Department of University Hospital and the dynamics of Schumann resonances were studied from January 1, 2016 to December 31, 2016. Results. It was found that changes in the strength of the Earth’s local magnetic field in 2016 and 2021 were comparable and corresponded to the characteristic annual dynamics of the Earth’s local electromagnetic fields. This made it possible to conduct a comparative analysis of annual correlation graphs and establish general trends in the dynamics of indicators and graphical similarities. It confirmed the pre­sence of a general dependence of reactions to the external electromagnetic field of the Earth in female and male patients both with nephrological pathology and myocardial infarction. In nephrological patients of both sexes, all correlation coefficients in all ranges of Schumann resonances were positive. The only negative correlation coefficient P5 (SGamma) [32; 65] Hz (r = –0.069; p = 0.313) was in the female group. This fact as well as the presence of a significant dynamics of the correlation coefficient P5 (SGamma) [32; 65] Hz (r = 0.009; p = 0.475) in the male group indicate that higher magnetic field strength in this frequency range may be associated with a reduced incidence of kidney disease. We obtained data that a higher magnetic field intensity in the gamma range from 32 to 65 Hz as a pathogenetic component can contribute to the destabilization of the cardiovascular system, but at the same time it is associated with a positive effect on the state of nephrological pathology. Based on this, we can tentatively assume the opposite direction of the Earth’s electromagnetic field influence on the pathogenetic mechanisms of renal and cardiovascular diseases. This is clearly demonstrated by comparing the correlation coefficients between the incidence of kidney disease and the occurrence of myocardial infarction in men and women. The Earth’s stronger magnetic field in the gamma range contributes to an increase in the incidence of myocardial infarction, which is confirmed by the large number of patients during this period. Under these same conditions, a decrease in the incidence of kidney disease has been detected. This opposite direction is observed in both sexes. But in women the reaction is stronger, which is confirmed by a larger difference in correlation coefficients. Conclusions. 1. Changes in the Earth’s electromagnetic field are related to the functional state of the cardiovascular system and the condition of the kidneys. 2. It can be assumed that the effect of the Earth’s electromagnetic field on the pathogenetic mechanisms of kidney disease is in the opposite direction of that on the cardiovascular one. 3. Reliable gender differences in correlations between the influence of changes in the local Schumann resonance on the functional state of the cardiovascular system and kidneys were not found. 4. The connection of the Earth’s local geomagnetic field with kidney function may be another new unexplored pathogenetic mechanism in cardiorenal syndrome and noncommunicable chronic diseases.

Information-incorporated gene network construction with FDR control.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. R package is freely available for download at https://cran.r-project.org/package=PCGII.

Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes.

Typically, industrial processes possess both temporal and spatial dependencies due to intravariable dynamics and intervariable couplings. The two dependencies have different manifestations, indicating diverse process characteristics. However, the existing methods fail to separate temporal and spatial information well, leading to inappropriate representation and inaccurate fault detection and isolation results. This study proposes an explicit representation and customized fault isolation framework to tackle temporal and spatial characteristics, so as to identify and locate anomalies affecting different dependencies. First, we design a double-level separation method for temporal and spatial information. In the first level, we construct two independent auto-encoding modules to extract temporal correlation and spatial graph structure in parallel. In the second level, we propose an information aliasing loss function to guild the two modules to distinguish between temporal and spatial characteristics, further facilitating information separation. By monitoring the explicit temporal and spatial statistics obtained by the two modules, spatiotemporal dependencies of anomalies can be determined for subsequent isolation. Furthermore, we propose a customized isolation strategy for anomalies in temporal and spatial characteristics. By quantifying changes in intravariable temporal dynamics and intervariable spatial graph structure individually, temporal impact and spatial propagation of faults can be finely characterized and isolated. Three examples are adopted to verify the performance of the proposed framework, including a numerical example, a real condensing system of the thermal power plant process, and the Tennessee Eastman benchmark process.

Research and Application of Power Grid Data Anomaly Tracing Method Based on Time Series Correlation

Abstract Power grid data is a “barometer” that reflects the operational trend of the power grid system, so conducting a traceability study on abnormal data generated by the power grid system is crucial to maintaining the stable operation of the power grid system and preventing further malfunctions. This article proposes a maximum time series correlation ring tracing algorithm based on time series correlation: firstly, for a large amount of time series data collected from measurement points in the power grid system, a correlation coefficient matrix is calculated, and then a time series correlation graph is constructed using graph theory knowledge. The Kruskal algorithm is used to search for the longest spanning tree in the time series correlation graph. Finally, based on the spanning tree, the breadth-first search (BFS) algorithm is further used to obtain the maximum time series correlation ring[1]. By using the time series correlation of each node within the ring and the physical topological relationship between nodes, the abnormal data can be traced back. Distinguish whether the generation of abnormal data is due to a single point failure or system failure. Most of the existing fault tracing techniques are based on machine learning algorithms, which are difficult to widely apply in high-dimensional time series data due to their high complexity.Through experiments on real power grid data, the results verify the effectiveness of this method in anomaly tracing of high-dimensional time series data. Through comparative experiments, this method is superior to machine learning model algorithms in terms of efficiency and stability. At the same time, this method does not require additional calculations of the statistical distribution of the samples to be tested, thus greatly saving computational costs.

Semi-supervised medical image classification via distance correlation minimization and graph attention regularization

We propose a novel semi-supervised learning method to leverage unlabeled data alongside minimal annotated data and improve medical imaging classification performance in realistic scenarios with limited labeling budgets to afford data annotations. Our method introduces distance correlation to minimize correlations between feature representations from different views of the same image encoded with non-coupled deep neural networks architectures. In addition, it incorporates a data-driven graph-attention based regularization strategy to model affinities among images within the unlabeled data by exploiting their inherent relational information in the feature space. We conduct extensive experiments on four medical imaging benchmark data sets involving X-ray, dermoscopic, magnetic resonance, and computer tomography imaging on single and multi-label medical imaging classification scenarios. Our experiments demonstrate the effectiveness of our method in achieving very competitive performance and outperforming several state-of-the-art semi-supervised learning methods. Furthermore, they confirm the suitability of distance correlation as a versatile dependence measure and the benefits of the proposed graph-attention based regularization for semi-supervised learning in medical imaging analysis.

TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) annotates related labels for unknown text from large-scale label sets. Transformer-based methods have become the dominant approach for solving the XMTC task due to their effective text representation capabilities. However, the existing Transformer-based methods fail to effectively exploit the correlation between labels in the XMTC task. To address this shortcoming, we propose a novel model called TLC-XML, i.e., a Transformer with label correlation for extreme multi-label text classification. TLC-XML comprises three modules: Partition, Matcher and Ranker. In the Partition module, we exploit the semantic and co-occurrence information of labels to construct the label correlation graph, and further partition the strongly correlated labels into the same cluster. In the Matcher module, we propose cluster correlation learning, which uses the graph convolutional network (GCN) to extract the correlation between clusters. We then introduce these valuable correlations into the classifier to match related clusters. In the Ranker module, we propose label interaction learning, which aggregates the raw label prediction with the information of the neighboring labels. The experimental results on benchmark datasets show that TLC-XML significantly outperforms state-of-the-art XMTC methods.

Study on the Material Models of the Forming Limit Curves Development for Predicting a Fracture Behavior of AA5754-O in Automotive Parts

The forming limit curve (FLC) is commonly used to predict the formability behavior of sheet metal after the forming process. In this research, the forming limit curve generated from the Materials Model was applied to analyze and predict the fracture behavior of the fuel tank workpiece, a motorcycle part made of AA5754-O material, using the deep drawing process simulated by the finite element method. The research involved a comparison with actual cracks that occur in the automotive industry after molding. To determine the mechanical properties of the AA5754-O material for use in the forming limit curve, a specimen with a thickness of 1.5 mm was subjected to a tensile strength test, providing the necessary input for the mechanical properties in the forming limit curve based on the Keeler-Beizer equation. The forming limit curve is a correlation graph between major strain and minor strain. When the FLC is created from the Materials Model, it is utilized in conjunction with deep drawing drag simulation in the PAM-STAMP program to predict the fracture point. The accuracy of the mathematically generated FLC in predicting fracture behavior was verified after the deep drawing process. The study found that the FLC based on the Keeler-Beizer equation can accurately predict the cracking behavior of AA5754-O sheet metal, enabling identification of the fracture location during the deep drawing process. One advantage of creating the FLC from the material models is its compatibility with the same material but with different workpiece shapes, allowing its use in conjunction with molding simulations using various programs. This approach saves costs associated with testing to obtain the FLC.

Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Graphical representation of data prediction potential: correlation graphs and correlation chains

The correlation of the set of attributes is a crucial statistical value for the measuring of prediction potential present in a dataset. The correlation coefficient, which measures the correlation between the values of two attributes, can be used in order to measure the prediction potential between two-element subsets of a dataset containing a high number of attributes. In this way two common summary visualizations of prediction potential in datasets are formed—correlation matrices and correlation heatmaps. Both of these visualizations are focused on the presentation of correlation between pair of attributes but not much more regarding the context of correlations in the dataset. The main objective of this article is the design and implementation of graphical models usable in a visual representation of data prediction potential—correlation graphs and correlation chains—which emphasize the pseudo-transitivity of prediction potential in a dataset.

ScENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding.

Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.

Disentangling the effects of PTSD from Gulf War Illness in male veterans via a systems-wide analysis of immune cell, cytokine, and symptom measures

BackgroundOne-third of veterans returning from the 1990–1991 Gulf War reported a myriad of symptoms including cognitive dysfunction, skin rashes, musculoskeletal discomfort, and fatigue. This symptom cluster is now referred to as Gulf War Illness (GWI). As the underlying mechanisms of GWI have yet to be fully elucidated, diagnosis and treatment are based on symptomatic presentation. One confounding factor tied to the illness is the high presence of post-traumatic stress disorder (PTSD). Previous research efforts have demonstrated that both GWI and PTSD are associated with immunological dysfunction. As such, this research endeavor aimed to provide insight into the complex relationship between GWI symptoms, cytokine presence, and immune cell populations to pinpoint the impact of PTSD on these measures in GWI.MethodsSymptom measures were gathered through the Multidimensional fatigue inventory (MFI) and 36-item short form health survey (SF-36) scales and biological measures were obtained through cytokine & cytometry analysis. Subgrouping was conducted using Davidson Trauma Scale scores and the Structured Clinical Interview for Diagnostic and statistical manual of mental disorders (DSM)-5, into GWI with high probability of PTSD symptoms (GWIH) and GWI with low probability of PTSD symptoms (GWIL). Data was analyzed using Analysis of variance (ANOVA) statistical analysis along with correlation graph analysis. We mapped correlations between immune cells and cytokine signaling measures, hormones and GWI symptom measures to identify patterns in regulation between the GWIH, GWIL, and healthy control groups.ResultsGWI with comorbid PTSD symptoms resulted in poorer health outcomes compared with both Healthy control (HC) and the GWIL subgroup. Significant differences were found in basophil levels of GWI compared with HC at peak exercise regardless of PTSD symptom comorbidity (ANOVA F = 4.7, P = 0.01,) indicating its potential usage as a biomarker for general GWI from control. While the unique identification of GWI with PTSD symptoms was less clear, the GWIL subgroup was found to be delineated from both GWIH and HC on measures of IL-15 across an exercise challenge (ANOVA F > 3.75, P < 0.03). Additional differences in natural killer (NK) cell numbers and function highlight IL-15 as a potential biomarker of GWI in the absence of PTSD symptoms.ConclusionWe conclude that disentangling GWI and PTSD by defining trauma-based subgroups may aid in the identification of unique GWI biosignatures that can help to improve diagnosis and target treatment of GWI more effectively.

Nomogram model based on clinical factors and autonomic nervous system activity for predicting residual renal function decline in patients undergoing peritoneal dialysis.

Several heart rate variability (HRV) parameters were reported to be associated with residual renal function (RRF) in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). However, it is unclear whether using HRV or other autonomic nervous system (ANS) activity indexes can predict RRF decline in CAPD patients. Patients undergoing CAPD in 2022 from the First Affiliated Hospital of Nanjing Medical University were enrolled in this study. Their clinical characteristics, 5-min HRV parameters and average voltage of 5-min skin sympathetic nerve activity (aSKNA) were collected. According to the 12-month glomerular filtration rate (GFR) decline rate compared with the upper quartile, these patients were categorized into two groups: RRF decline (RRF-D) group and RRF stable (RRF-S) group. Clinical factors and ANS activity indexes for predicting 1-year RRF decline were analyzed using logistic regression, and a nomogram model was further established. The relationships between volume load related indexes and aSKNA were displayed by Spearman's correlation graphs. Ninety-eight patients (53 women, average age of 46.7 ± 13.0 years old) with a median dialysis vintage of 24.5 months were enrolled in this study. Seventy-three patients were categorized into the RRF-S group and 25 patients into the RRF-D group. Compared with RRF-S group, patients in the RRF-D group had higher systolic blood pressure (BP; p = 0.019), higher GFR (p = 0.016), higher serum phosphorous level (p = 0.030), lower total Kt/V (p = 0.001), and lower levels of hemoglobin (p = 0.007) and albumin (p = 0.010). The RRF-D group generally exhibited lower HRV parameters and aSKNA compared with the RRF-S group. A nomogram model included clinical factors (sex, systolic BP, hemoglobin, GFR, and total Kt/V) and aSKNA showed the largest AUC of 0.940 (95% CI: 0.890-0.990) for predicting 1-year RRF decline. The nomogram model included clinical factors (sex, systolic BP, hemoglobin, GFR and total Kt/V) and ANS activity index (aSKNA) might be a promising tool for predicting 1-year RRF decline in CAPD patients.

A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke Rehabilitation.

The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adapting of therapy. In this paper, clinicians rated task, segment, and composite movement feature performance for 478 videos of stroke survivors executing upper extremity therapy tasks. We used the clinician ratings to develop a Hierarchical Bayesian Model (HBM) with task, segment, and composite layers for computing the statistical relation of movement quality changes to function. The model was enhanced through a detailed correlation graph (ΔHBM) that links computationally extracted kinematics with clinician-rated composite features for different task-segment combinations. Utilizing the weights and correlation graphs, we finally derive reverse cascading probabilities of the proposed HBM from kinematics to composite features, segments, and tasks. In a test involving 98 cases where clinician ratings differed, the HBM resolved 95% of these discrepancies. The model effectively aligned kinematic data with specific task-segment combinations in over 90% of cases. Once the HBM is expanded and refined through additional data it can be used for the automated calculation of statistical relations between changes in kinematics and performance of functional tasks and the generation of therapy assessment recommendations for clinicians. While our work primarily focuses on the upper extremities of stroke survivors, the HBM can be adapted to many other neurorehabilitation contexts.

Learning Multi-Frequency Partial Correlation Graphs

Learning Multi-Frequency Partial Correlation Graphs