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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. Research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷 103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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Benchmarking in Optimization: Best Practice and Open Issues

arXiv

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arXiv 2020年

作者： Bartz-Beielstein, Thomas Doerr, Carola Bossek, Jakob Chandrasekaran, Sowmya Eftimov, Tome Fischbach, Andreas Kerschke, Pascal López-Ibáñez, Manuel Malan, Katherine M. Moore, Jason H. Naujoks, Boris Orzechowski, Patryk Volz, Vanessa Wagner, Markus Weise, Thomas Institute for Data Science Engineering and Analytics TH Köln Germany Sorbonne Université CNRS LIP6 Paris France Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Statistics and Optimization Group University of Münster Münster Germany School of Computer Science and Engineering University of Málaga Málaga Spain Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Automatics AGH University of Science and Technology Krakow Poland modl.ai Copenhagen Denmark Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei China

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic *** Codes 68W50. Copyright © 2020, The Authors. All rights reserved.

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Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

Structured Information Extraction of Pathology Reports with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Kaiwen Tang Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiaotong University Xi’an Shaanxi China School of Computer Science and Technology Qian Xuesen College Xi’an Jiaotong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist's workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.

关键词： Pathology Cancer Task analysis Annotations Information retrieval Medical diagnostic imaging data mining

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OpEn: Code Generation for Embedded Nonconvex Optimization *

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IFAC-PapersOnLine 2020年第2期53卷 6548-6554页

作者： Pantelis Sopasakis Emil Fresk Panagiotis Patrinos Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science (EEECS) and Centre for Intelligent and Autonomous Manufacturing Systems (i-AMS) Ashby Building Stranmillis Road BT9 5AG Belfast Northern Ireland WideFind AB Aurorum 1C 977 75 Luleå Sweden KU Leuven Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics & Optimization in Engineering (OPTEC) Kasteelpark Arenberg 10 3001 Leuven Belgium

We present Optimization Engine (OpEn): an open-source code generation framework for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of nonconvex problems. The proposed method involves very simple algebraic operations such as vector products, has a low memory footprint and exhibits very good convergence properties that allow the solution of nonconvex problems on embedded devices. OpEn’s core solver is written is Rust — a modern, high-performance, memory-safe and thread-safe systems programming language — while users can call it from Python, MATLAB, C, C++, ROS or over a TCP socket.

关键词： Embedded numerical optimization nonconvex optimization problems code generation model predictive control moving horizon estimation Rust Robot Operating System

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A contingency-based approach to data governance design for platform ecosystems 2

A contingency-based approach to data governance design for p...

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22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018

作者： Lee, Sung Une Zhu, Liming Jeffery, Ross Architecture and Analytics Platforms Group Data61 CSIRO Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9784902590838

data today is regarded as a new type of fuel of platform ecosystems. It enables sustainable growth of platform ecosystems through users' data contribution. data governance is necessary to safely manage platform data and succeed in business. Platform ecosystems, however, encounter a complicated business context and environment since there are multiple participating groups, different strategies, goals, and different levels of market regulations. How to adopt appropriate data governance dealing with the concerns has been neglected, and therefore there is little research on this topic. To respond to the challenge, we introduce a novel design approach which can address the various contingencies, characteristics and governance goals of platform ecosystems. We present a case study and use case to illustrate the implications and support the implementation of the approach in practice. © PACIS 2018.

关键词： Ecosystems

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Sub-architecture ensemble pruning in neural architecture search

arXiv

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arXiv 2019年

作者： Bian, Yijun Song, Qingquan Du, Mengnan Yao, Jun Chen, Huanhuan Hu, Xia The School of Computer Science and Technology University of Science and Technology of China Hefei230027 China The Department of Computer Science and Engineering Texas A&M University College StationTX77840 United States The Data Science and Analytics Department WeBank Shenzhen518000 China

Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computations that might not be affordable for researchers and practitioners. While recent attempts have employed ensemble learning methods to mitigate the enormous computational cost, however, they neglect a key property of ensemble methods, namely diversity, which leads to collecting more similar sub-architectures with potential redundancy in the final design. To tackle this problem, we propose a pruning method for NAS ensembles called "Sub-Architecture Ensemble Pruning in Neural Architecture Search (SAEP)." It targets to leverage diversity and to achieve sub-ensemble architectures at a smaller size with comparable performance to ensemble architectures that are not pruned. Three possible solutions are proposed to decide which sub-architectures to prune during the searching process. Experimental results exhibit the effectiveness of the proposed method by largely reducing the number of sub-architectures without degrading the performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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Pooling Approach for Task Allocation in the Blockchain Based Decentralized Storage Network

Pooling Approach for Task Allocation in the Blockchain Based...

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International Conference on Network and Service Management

作者： Iman Vakilinia Shahin Vakilinia Shahriar Badsha Engin Arslan Shamik Sengupta School of Computing University of North Florida Jacksonville FL USA yPwC Data Analytics Team Montreal QC CA Computer Science and Engineering Department University of Nevada Reno NV USA

ISBN: (数字)9783903176249

ISBN: (纸本)9781728153964

Blockchain technology has provided a solid system to develop incentivization algorithms using the smart contract. Blockchain applies the distributed ledger to store transaction histories, and the information is stored across a network of computers instead of on a single server. This facilitates the development of a new set of applications such as distributed file storage systems where users can rent out their storage in return for a premium. The distributed file storage systems provide more privacy and security compared to the centralized storage models as there is no need to have a trusted party. New schemes have been developed for distributed file storage systems on top of the blockchain platform, however, the problem of task/service allocation in these models have not been studied before. In this paper, we study the task/service allocation in the distributed file storage systems considering the challenge of computation cost. First, we formalize the problem of task/service allocation in a decentralized storage network, and then we discuss different approaches to allocate storage tasks to storage servers in an efficient manner. Moreover, we study the benefits of the cooperation (a.k.a pooling) in the storage and retrieval markets of distributed storage networks. The evaluation results show the benefit of our proposed pooling based approach in storage and retrieval markets.

关键词： Task analysis Servers Contracts Resource management Computational modeling Internet

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Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors

arXiv

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arXiv 2023年

作者： Heming, Carolina A.M. Abdalla, Mohamed Mohanna, Shahram Ahluwalia, Monish Zhang, Linglin Trivedi, Hari Woo, MinJae Fine, Benjamin Gichoya, Judy Wawira Celi, Leo Anthony Seyyed-Kalantari, Laleh University of Iowa Iowa CityIA United States Institute for Better Health Trillium Health Partners 100 Queensway West 6th floor MississaugaONL5B 1B8 Canada Kingston Health Sciences Centre Queen's University 76 Stuart St. KingstonONK7L 2V7 Canada School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Vector Institute of Artificial Intelligence 661 University Ave Suite 710 TorontoONM5G 1M1 Canada Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Harvard Medical School 25 Shattuck St BostonMA02115 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada Department of Medicine University of Alberta Walter C Mackenzie Health Sciences Centre St. NW Edmonton Alberta 8440 112 Canada

The model facts labels are often used to communicate artificial intelligence (AI) model performance to the end users for proper deployment and often report the overall performance averaged across all populations. However, the overall performance is not generalizable to different subgroups of patients. In this paper, we highlight the importance of currently unforeseen aspects, essential for AI models in healthcare, that are not included in the model fact cards. We suggest including through bias and fairness analysis outcomes in model fact labels in the health care AI model including: (a) socio-demographic fairness factors (e.g., sexes, race, socioeconomic status, etc.) and (b) Technical, Environmental and Biological Bias Factors (e.g. disease-dependent, anatomical, or instrumental factors, etc.). The socio-demographic fairness factors have been explored in fairness analysis of AI models in healthcare but have not often been listed in model fact labels. Besides, the technical, environmental, and biological bias factors have not often been explored in the bias analysis of AI models in healthcare and have not been reported in model fact cards. Here, we demonstrate disparities with respect to both sets of factors. We suggest that clinical AI model reporting cards need to be expanded to communicate such a wider range of disparities to end users for precise patient care. Analysis and communication of such a wider range of biases identify potential drivers of bias, which pave the way to debiasing. As well, it highlights the model caveats and guides the end user and policymaker on how, when, and under what circumstances the model works or fails. Copyright © 2023, The Authors. All rights reserved.

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GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization

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ACM Transactions on Information Systems 2018年第3期36卷 1–29页

作者： Lian, Defu Zheng, Kai Ge, Yong Cao, Longbing Chen, Enhong Xie, Xing Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Management Information Systems University of Arizona TucsonAZ85721 United States Advanced Analytics Institute University of Technology Sydney NSW Australia School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Microsoft Research Beijing100080 China

Location recommendation is an important means to help people discover attractive locations. However, extreme sparsity of user-location matrices leads to a severe challenge, so it is necessary to take implicit feedback characteristics of user mobility data into account and leverage the location's spatial information. To this end, based on previously developed GeoMF, we propose a scalable and flexible framework, dubbed GeoMF++, for joint geographical modeling and implicit feedback-based matrix factorization. We then develop an efficient optimization algorithm for parameter learning, which scales linearly with data size and the total number of neighbor grids of all locations. GeoMF++ can be well explained from two perspectives. First, it subsumes two-dimensional kernel density estimation so that it captures spatial clustering phenomenon in user mobility data;Second, it is strongly connected with widely used neighbor additive models, graph Laplacian regularized models, and collectivematrix factorization. Finally, we extensively evaluate GeoMF++ on two large-scale LBSN datasets. The experimental results show that GeoMF++ consistently outperforms the state-of-the-art and other competing baselines on both datasets in terms of NDCG and Recall. Besides, the efficiency studies show that GeoMF++ is much more scalable with the increase of data size and the dimension of latent space. © 2018 ACM.

关键词： Location

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