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1. User recommendation in healthcare social media by assessing user similarity in heterogeneous network

   https://doi.org/http://dx.doi.org/10.1016/j.artmed.2017.03.002  

   Jiang, Ling ; Yang, Christopher C. ( September 2017 , Artificial intelligence in medicine)

   Objective: The rapid growth of online health social websites has captured a vast amount of healthcare information and made the information easy to access for health consumers. E-patients often use these social websites for informational and emotional support. However, health consumers could be easily overwhelmed by the overloaded information. Healthcare information searching can be very difficult for consumers, not to mention most of them are not skilled information searcher. In this work, we investigate the approaches for measuring user similarity in online health social websites. By recommending similar users to consumers, we can help them to seek informational and emotional support in a more efficient way. Methods: We propose to represent the healthcare social media data as a heterogeneous healthcare information network and introduce the local and global structural approaches for measuring user similarity in a heterogeneous network. We compare the proposed structural approaches with the content-based approach. Results: Experiments were conducted on a data set collected from a popular online health social website,and the results showed that content-based approach performed better for inactive users, while structural approaches performed better for active users. Moreover, global structural approach outperformed local structural approach for all user groups. In addition, we conducted experiments on local and global structural approaches using different weight schemas for the edges in the network. Leverage performed the best for both local and global approaches. Finally, we integrated different approaches and demonstrated that hybrid method yielded better performance than the individual approach. Conclusion: The results indicate that content-based methods can effectively capture the similarity of inactive users who usually have focused interests, while structural methods can achieve better performance when rich structural information is available. Local structural approach only considers direct connections between nodes in the network, while global structural approach takes the indirect connections into account. Therefore, the global similarity approach can deal with sparse networks and capture the implicit similarity between two users. Different approaches may capture different aspects of the similarity relationship between two users. When we combine different methods together, we could achieve a better performance than using each individual method. 

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2. Multiscale combination of physically-based registration and deformation modeling

   https://doi.org/10.1109/CVPR.2000.854870  

   Tsap, L.V. ; Goldgof, D.B. ; Sarkar, S. ( August 2000 , IEEE Conference on Computer Vision and Pattern Recogniton)

   In this paper we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea of our approach is that a finite element (FEM) model can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdorff distance to recover correspondences. The method is evaluated with respect to speed, accuracy, and noise sensitivity. Advantages of the proposed approach are demonstrated using man-made elastic materials and human skin motion. Experiments with regular grid features are used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown that the new method does not require a grid and can adapt the model to available object features. 

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3. Performance Analysis of Corrosion Resistant Electroless Nickel-Plated Impinging Computer Numerical Control Manufactured Liquid Cooling Cold Plate

   https://doi.org/10.1115/1.4054972  

   Radmard, Vahideh ; Gharaibeh, Ahmad R. ; Tradat, Mohammad I. ; Hoang, Cong. H. ; Manaserh, Yaman Yaseen ; Nemati, Kourosh ; Schiffres, Scott N. ; Sammakia, Bahgat G. ( June 2023 , Journal of electronic packaging)

   More than ever before, data centers must deploy robust thermal solutions to adequately host the high-density and high-performance computing that is in high demand. The newer generation of central processing units (CPUs) and graphics processing units (GPUs) has substantially higher thermal power densities than previous generations. In recent years, more data centers rely on liquid cooling for the high-heat processors inside the servers and air cooling for the remaining low-heat information technology equipment. This hybrid cooling approach creates a smaller and more efficient data center. The deployment of direct-to-chip cold plate liquid cooling is one of the mainstream approaches to providing concentrated cooling to targeted processors. In this study, a processor-level experimental setup was developed to evaluate the cooling performance of a novel computer numerical control (CNC) machined nickel-plated impinging cold plate on a 1 in.  1 in. mock heater that represents a functional processing unit. The pressure drop and thermal resistance performance curves of the electroless nickel-plated cold plate are compared to those of a pure copper cold plate. A temperature uniformity analysis is done using compuational fluid dynamics and compared to the actual test data. Finally, the CNC machined pure copper one is compared to other reported cold plates to demonstrate its superiority of the design with respect to the cooling performance. 

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4. Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection

   https://doi.org/10.1109/ICHI.2018.00010  

   Zhao, Mengnan ; Yang, Christopher C. ( June 2018 , Proceedings of the IEEE International Conference on Healthcare Informatics 2018)

   Off-label drug use is an important healthcare topic as it is quite common and sometimes inevitable in medical practice. Though gaining information about off-label drug uses could benefit a lot of healthcare stakeholders such as patients, physicians, and pharmaceutical companies, there is no such data repository of such information available. There is a desire for a systematic approach to detect off-label drug uses. Other than using data sources such as EHR and clinical notes that are provided by healthcare providers, we exploited social media data especially online health community (OHC) data to detect the off-label drug uses, with consideration of the increasing social media users and the large volume of valuable and timely user-generated contents. We adopted tensor decomposition technique, CP decomposition in this work, to deal with the sparsity and missing data problem in social media data. On the basis of tensor decomposition results, we used two approaches to identify off-label drug use candidates: (1) one is via ranking the CP decomposition resulting components, (2) the other one is applying a heterogeneous network mining method, proposed in our previous work [9], on the reconstructed dataset by CP decomposition. The first approach identified a number of significant off-label use candidates, for which we were able to conduct case studies and found medical explanations for 7 out of 12 identified off-label use candidates. The second approach achieved better performance than the previous method [9] by improving the F1-score by 3%. It demonstrated the effectiveness of performing tensor decomposition on social media data for detecting off-label drug use. 

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5. A Generalizable Deep-Learning Approach for Cardiac Magnetic Resonance Image Segmentation Using Image Augmentation and Attention U-Net

   https://doi.org/10.1007/978-3-030-68107-4_29  

   Kong, F ; Shadden, SC ( January 2021 , Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020)

   Puyol Anton, E ; Pop, M ; Sermesant, M ; Campello, V ; Lalande, A ; Lekadir, K ; Suinesiaputra, A ; Camara, O ; Young, A (Ed.)

   Cardiac cine magnetic resonance imaging (CMRI) is the reference standard for assessing cardiac structure as well as function. However, CMRI data presents large variations among different centers, vendors, and patients with various cardiovascular diseases. Since typical deep-learning-based segmentation methods are usually trained using a limited number of ground truth annotations, they may not generalize well to unseen MR images, due to the variations between the training and testing data. In this study, we proposed an approach towards building a generalizable deep-learning-based model for cardiac structure segmentations from multi-vendor,multi-center and multi-diseases CMRI data. We used a novel combination of image augmentation and a consistency loss function to improve model robustness to typical variations in CMRI data. The proposed image augmentation strategy leverages un-labeled data by a) using CycleGAN to generate images in different styles and b) exchanging the low-frequency features of images from different vendors. Our model architecture was based on an attention-gated U-Net model that learns to focus on cardiac structures of varying shapes and sizes while suppressing irrelevant regions. The proposed augmentation and consistency training method demonstrated improved performance on CMRI images from new vendors and centers. When evaluated using CMRI data from 4 vendors and 6 clinical center, our method was generally able to produce accurate segmentations of cardiac structures. 

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