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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

Deteriorative reactions of Pacific whiteleg shrimp ( Litopenaeus vannamei ) during chilled storage: enzymatic degradation, microbial activity, and quality changes

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Journal of Agriculture and Food Research

Journal of Agriculture and Food Research 2025年 22卷

作者： Jun Liang Shutian Sun Xinpei Qi Zhenyu Han Huijue Kong Difei Luo Xiaoyu Luo Food Science and Technology Program Department of Life Sciences Beijing Normal-Hong Kong Baptist University Zhuhai Guangdong 519087 China Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science Beijing Normal-Hong Kong Baptist University Zhuhai Guangdong 519087 China

Pacific whiteleg shrimp ( Litopenaeus vannamei ) is one of the signature aquatic products in the city of Zhuhai and the Greater Bay area of southern China. Due to its high perishability under ambient conditions, the deterioration during storage can result in the decline of eating quality and nutrient loss. This study investigated the deteriorative mechanisms of Pacific whiteleg shrimp during 7-day storage at 4 °C, especially on enzymatic autolysis, oxidative reactions, and microbial growth. Comparing with fresh shrimp, on day 7, significant higher pH (7.70), total volatile basic nitrogen content (81.32 mg/100 g), and peroxide value (17.09 mg hydroperoxide/kg) were found ( p < 0.05), demonstrating the accumulation of alkaline amine metabolites and higher degree of lipid oxidation. Shrimp protein degradation was revealed by the increase of peptides, loss of sulfhydryl groups, as well as activity decline of endogenous enzymes. Vibrio, Photobacterium , Fusibacter , Pseudomonas , Ralstonia and Shewanella were identified as the predominant bacterial strains in fresh shrimp samples, while lactic acid bacteria were found to enumerate substantially during shrimp deterioration. Pearson's correlation analysis was performed in this study, and significant strong correlations were found between the chemical degradative reactions and microbial growth, as well as the shrimp textural changes. The deteriorative mechanisms of Pacific whiteleg shrimp explored in this study can serve as the fundamental knowledge for developing advanced seafood preservation techniques or active packaging materials in the future.

关键词： Litopenaeus vannamei Shrimp deterioration Endogenous enzymatic activity Lipid oxidation Protein degradation

Graph Representation Learning in Biomedicine

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arXiv 2021年

作者： Li, Michelle M. Huang, Kexin Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Health Data Science Program Harvard T.H. Chan School of Public Health BostonMA02115 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States

Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. Advances in artificial intelligence, specifically deep learning, have enabled us to model, analyze, and learn with such networked data. In this review, we put forward an observation that long-standing principles of systems biology and medicine-while often unspoken in machine learning research-provide the conceptual grounding for representation learning on graphs, explain its current successes and limitations, and even inform future advancements. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces. We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning. Exemplary domains covered include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines. © 2021, CC BY.

关键词： Vector spaces

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

Factors that affect the technological transition of firms toward the industry 4.0 technologies

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arXiv 2022年

作者： Kim, Seung Hwan Jeon, Jeong hwan Aridi, Anwar Jun, Bogang Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of Department of Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Ulsan Korea Republic of Finance Competitiveness and Innovation Global Practice Europe and Central Asia Unit The World Bank Group Department of Economics Inha University Korea Republic of Department of Data Science Inha University Korea Republic of Research Center for Small Businesses Ecosystem Inha University Korea Republic of

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms’ patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm’s footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support on R&D activities, we further analyzed the role of government policy in supporting firms’ knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms’ technological capabilities into I4Ts. © 2022, CC BY.

关键词： Industry 4.0

Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors

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arXiv 2020年

作者： Cao, Jian Durante, Daniele Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Saudi Arabia Department of Decision Sciences Bocconi Institute for Data Science and Analytics Bocconi University Italy Statistics Program King Abdullah University of Science and Technology Saudi Arabia

Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling the relationship between the observed predictors and the binary responses. A widely-implemented solution is to express the probability parameter via a probit mapping of a Gaussian process indexed by predictors. However, unlike for continuous settings, there is a lack of closed-form results for predictive distributions in binary models with Gaussian process priors. Markov chain Monte Carlo methods and approximation strategies provide common solutions to this problem, but state-of-the-art algorithms are either computationally intractable or inaccurate in moderate-to-high dimensions. In this article, we aim to cover this gap by deriving closed-form expressions for the predictive probabilities in probit Gaussian processes that rely either on cumulative distribution functions of multivariate Gaussians or on functionals of multivariate truncated normals. To evaluate these quantities we develop novel scalable solutions based on tile-low-rank Monte Carlo methods for computing multivariate Gaussian probabilities, and on mean-field variational approximations of multivariate truncated normals. Closed-form expressions for the marginal likelihood and for the posterior distribution of the Gaussian process are also discussed. As shown in simulated and real-world empirical studies, the proposed methods scale to dimensions where state-of-the-art solutions are impractical. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

Two-Month Cognitive Changes Enhance Prediction of Nonremission in Clinical High-Risk Individuals

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Biological Psychiatry 2025年第2期98卷 175-185.0页

作者： Zhang, TianHong Wei, YanYan Tang, XiaoChen Xu, LiHua Cui, HuiRu Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Tang, YingYing Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Longitudinal changes in cognitive function may be crucial in predicting clinical outcomes in clinical high-risk (CHR) individuals. In this study, we aimed to investigate the predictive value of baseline cognitive impairment and short-term cognitive changes for nonremission and conversion to psychosis in individuals at CHR for psychosis compared with healthy control individuals (HCs). Methods: This study used a multiple-group prospective design with a 3-year follow-up. CHR individuals and HCs were assessed at baseline and at a 2-month follow-up. Neuropsychological performance was evaluated using the Chinese version of the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery. Results: The study included 310 CHR individuals and 93 HCs. Significant improvements in predicting nonremission in CHR individuals were observed when incorporating cognitive changes over 2 months (area under the receiver operating characteristic curve [AUC] for baseline cognition, 0.690;AUC for changes, 0.819;z = 3.365, p < .001). Key predictors included the Hopkins Verbal Learning Test–Revised (β = 0.083, p = .003), Wechsler Memory Scale-III spatial span (β = 0.330, p < .001), and Brief Visuospatial Memory Test–Revised (β = 0.127, p < .001). Conversely, predicting conversion to psychosis showed no significant difference between baseline and 2-month cognitive changes (AUC for baseline cognition, 0.667;AUC for changes, 0.666;z = 0.021, p = .242). Conclusions: The findings underscore the importance of dynamic cognitive monitoring in CHR individuals. Short-term cognitive changes significantly enhanced the prediction of nonremission but did not add predictive value for conversion to psychosis beyond baseline assessments. Specific cognitive domains, such as verbal learning and working memory, were particularly valuable for predicting clinical outcomes. © 2025 Society of Biological Psychiatry

关键词： Cognition Functional outcome Prodromal psychosis Remission Transition Ultra-high risk