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Creators/Authors contains: "Zhang, Yufeng"

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  1. Abstract

    Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation.

    In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively.

    The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels.

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  2. In many species of animals, red carotenoid-based coloration is produced by metabolizing yellow dietary pigments, and this red ornamentation can be an honest signal of individual quality. However, the physiological basis for associations between organism function and the metabolism of red ornamental carotenoids from yellow dietary carotenoids remains uncertain. A recent hypothesis posits that carotenoid metabolism depends on mitochondrial performance, with diminished red coloration resulting from altered mitochondrial aerobic respiration. To test for an association between mitochondrial respiration and red carotenoids, we held wild-caught, molting male house finches in either small bird cages or large flight cages to create environmental challenges during the period when red ornamental coloration is produced. We predicted that small cages would present a less favorable environment than large flight cages and that captivity itself would decrease both mitochondrial performance and the abundance of red carotenoids compared to free-living birds. We found that captive-held birds circulated fewer red carotenoids, showed increased mitochondrial respiratory rates, and had lower complex II respiratory control ratios—a metric associated with mitochondrial efficiency—compared to free-living birds, though we did not detect a difference in the effects of small cages versus large cages. Among captive individuals, the birds that circulated the highest concentrations of red carotenoids had the highest mitochondrial respiratory control ratio for complex II substrate. These data support the hypothesis that the metabolism of red carotenoid pigments is linked to mitochondrial aerobic respiration in the house finch, but the mechanisms for this association remain to be established.

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  3. Simsekler, Mecit Can (Ed.)
    Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework ( l 2 -SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l 2 -SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l 2 -SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l 2 -SVMp+ achieves comparable or superior model performance compared to imputed privileged features. 
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    Free, publicly-accessible full text available June 29, 2024
  4. null (Ed.)
    The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training. 
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  5. A significant amount of research has been performed on network accessibility evaluation, but studies on incorporating accessibility maximization into network design problems have been relatively scarce. This study aimed to bridge the gap by proposing an integer programming model that explicitly maximizes the number of accessible opportunities within a given travel time budget. We adopted the Lagrangian relaxation method for decomposing the main problem into three subproblems that can be solved more efficiently using dynamic programming. The proposed method was applied to several case studies, which identified critical links for maximizing network accessibility with limited construction budget, and also illustrated the accuracy and efficiency of the algorithm. This method is promisingly scalable as a solution algorithm for large-scale accessibility-oriented network design problems. 
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  6. null (Ed.)