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Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study

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American Journal of Geriatric Psychiatry 2024年第2期32卷 244-255页

作者： Yeh, Tian-Shin Kang, Jiunn-Horng Littlejohns, Thomas J. Wu, Chia-Chieh Chen, Jin-Hua Piravej, Krisna Chiu, Wen-Ta Lam, Carlos Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Epidemiology and Nutrition Harvard T. H. Chan School of Public Health Harvard University Boston MA United States Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation College of Medicine National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Nuffield Department of Population Health University of Oxford Oxford United Kingdom Emergency Department Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Emergency School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Institutional Research Center Office of Data Science Taipei Medical University Taipei Taiwan Biostatistics Center Department of Medical Research Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Chula Neuroscience Center King Chulalongkorn Memorial Hospital Bangkok Thailand Graduate Institute of Injury Prevention and Control College of Public Health Taipei Medical University Taipei Taiwan AHMC Health System Alhambra CA United States

Objectives: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. Design: Single-center prospective cohort study. Setting: Emergency department of Wan Fang Hospital in Taiwan. Participants: Trauma patients aged 45 and older. Measurements: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. Results: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16–1.55);associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17–1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04–1.51), and falls (OR 1.23, 95% CI, 1.01–1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. Conclusion: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury. © 2023 American Association for Geriatric Psychiatry

关键词： Clinical Frailty Scale Frailty functional recovery injury middle-to older age

Higher-order rich-club phenomenon in collaborative research grant networks

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

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A Clinical Decision Support System of Pressure Ulcers Tissue...

5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

On the Optimization of Benefit to Cost Ratios for Public Sector Decision Making

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arXiv 2022年

作者： Miller, Frederick Kaya, Yaren Bilge Dimas, Geri L. Konrad, Renata Maass, Kayse Lee Trapp, Andrew C. Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA United States Data Science Program Worcester Polytechnic Institute WorcesterMA United States School of Business Worcester Polytechnic Institute WorcesterMA United States

Decision making in the public sector centers on delivering resources and services for the common good, emphasizing an expansive set of objectives such as equity and efficiency that accompany short term returns to reflect the broader cares of society and public beneficiaries. These contexts often feature multiple stakeholders and objectives operating in highly complex, real-world environments, where significant changes to practical operations must be balanced against the degree of deployment and change management. Cost-benefit analysis (CBA) is a prevailing decision-making paradigm in the public sector used to evaluate next best steps beyond the status quo ("do nothing") alternative. The benefit to cost ratio (BCR) is a common tool used in CBA to assess the potential of actions beyond maintaining the status quo, yet necessitates the ex ante identification and selection of a limited number of alternatives, which can be laborious and may lead to missed opportunities in the wider solution landscape. We introduce a new framework for public sector decision making that simultaneously considers a potentially wide range of alternatives, and identifies one that maximizes the BCR. Our framework implicitly generates and evaluates a large number of alternatives (actions), subject to original problem context conditions and augmented with action constraints that ensure movement beyond the status quo. We represent this problem as a mixed-integer linear fractional program (MILFP) and employ a version of Newton’s Method that converts the MILFP to a sequence of linearized mixed-integer programs, which retains tractability for fairly large problem instances. Our framework is applicable to a broad set of decision contexts that involve maximizing the BCR for marginal activity beyond the status quo. We demonstrate the applicability of our framework on a compelling case study for the New York City (NYC) runaway and homeless youth (RHY) shelter syste – an area of high societal need. Our opt

关键词： Decision making

Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN 1

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12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021

作者： Chen, Jun-Ting Huang, Yu-Cheng Roth, Holger Yang, Dong Lee, Chih-Kuo Lee, Wen-Jeng Wang, Tzung-Dau Chou, Cheng-Ying Wang, Weichung Graduate Program of Data Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Medical Imaging National Taiwan University Hospital Taipei100 Taiwan NVIDIA BethesdaMD United States Department of Internal Medicine National Taiwan University Hospital Hsin-Chu Branch Hsinchu Taiwan Cardiovascular Center and Divisions of Cardiology and Hospital Medicine Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan Department of Biomechatronics Engineering National Taiwan University Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan

ISBN: (数字)9783030937225

ISBN: (纸本)9783030937218

Measuring the existence of coronary artery plaques and stenoses is a standard way of evaluating the risk of cardiovascular diseases. Coronary Computed Tomography Angiography (CCTA) is one of the most common assessments of cardiovascular disease. Manually detecting stenoses over the complete coronary artery tree of each patient takes an expert about 10 min. Previous works about automated detection of plaques required carefully controlling parameters of region-based segmentation algorithms or hand-craft features extraction for machine learning algorithms. Current works presented the feasibility of deep learning algorithms for plaque and stenosis detection. This study aims to develop an efficient workflow for detecting plaques and classification using a 3D convolutional neural network (CNN). Based on a fully convolutional structure, our model has better stability than recurrent layers at the training stage. Furthermore, our method is efficient since the model takes only cross-sectional planes (CSP) images as the input, rather than including other views containing replicate information. For each patient, it takes less than 25 s to perform the whole workflow. The first step of our workflow is to extract CSP images on each control point of a given set of coronary artery centerlines. We then stack several contiguous CSP images together to form a 3D volume, which is the input of the CNN model. The model will predict the type of plaque (soft, mixed, or calcified) at each control point. With this information, we can not only detect the precise location of plaques but also summarize the existence of plaque in each artery segment. In addition, we consider the lumen radius estimation as the next step to approach stenosis detection. © 2022, Springer Nature Switzerland AG.

关键词： Heart

Metapopulation models imply non-Poissonian statistics of interevent times

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arXiv 2021年

作者： dos Reis, Elohim Fonseca Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, and this property impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from a patch to another according to the simple random walk. Our results hold true irrespectively of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

Empowering Biomedical Discovery with AI Agents

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arXiv 2024年

作者： Gao, Shanghua Fang, Ada Huang, Yepeng Giunchiglia, Valentina Noori, Ayush Schwarz, Jonathan Richard Ektefaie, Yasha Kondic, Jovana Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Department of Brain Sciences Imperial College London London United Kingdom Harvard College CambridgeMA United States Program in Biomedical Informatics Harvard Medical School BostonMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI’s ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies. © 2024, CC BY.

关键词： Adversarial machine learning

Artificial Intelligence for Digital and Computational Pathology

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology

Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement