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Using Novel Statistical Techniques to Accurately Determine the Predictive Dose Range in a Study of Overall Survival after Definitive Radiotherapy for Stage III Non-Small Cell Lung Cancer in Association with Heart Dose

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Journal of Cancer Therapy 2021年第9期12卷 505-529页

作者： Joshua R. Niska Jiuyun Hu Jing Li Michael G. Herman Cameron S. Thorpe Steven E. Schild Mirek Fatyga Department of Radiation Oncology Banner MD Anderson Cancer Center Phoenix Arizona USA School of Computing Informatics and Decision Systems Engineering Tempe Arizona USA School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta Georgia USA Department of Radiation Oncology Mayo Clinic Rochester Minnesota USA Department of Radiation Oncology Mayo Clinic Phoenix Arizona USA

Purpose: Recent studies of radiotherapy (RT) for stage III non-small-cell lung cancer (NSCLC) have associated high dose to the heart with cardiac toxicity and decreased overall survival (OS). We used advanced statisti... 详细信息

Purpose: Recent studies of radiotherapy (RT) for stage III non-small-cell lung cancer (NSCLC) have associated high dose to the heart with cardiac toxicity and decreased overall survival (OS). We used advanced statistical techniques to account for correlations between dosimetric variables and more accurately determine the range of heart doses which are associated with reduced OS in patients receiving RT for stage III NSCLC. Methods: From 2006 to 2013, 119 patients with stage III NSCLC received definitive RT at our institution. OS data was obtained from institutional tumor registry. We used multivariate Cox model to determine patient specific covariates predictive for reduced overall survival. We examined age, prescription dose, mean lung dose, lung V20, RT technique, stage, chemotherapy, tumor laterality, tumor volume, and tumor site as candidate covariates. We subsequently used novel statistical techniques within multivariate Cox model to systematically search the whole heart dose-volume histogram (DVH) for dose parameters associated with OS. Results: Patients were followed until death or 2.5 to 81.2 months (median 30.4 months) in those alive at last follow up. On multivariate analysis of whole heart DVH, the dose of 51 Gy was identified as a threshold dose above which the dose volume relationship becomes predictive for OS. We identified

关键词： Lung Cancer Cardiac Toxicity Lung Radiation Therapy Non-Small Cell Lung Cancer Radiation Toxicity

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Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness

arXiv

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arXiv 2020年

作者： Hong, Jinyung Pavlic, Theodore P. School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical Relational Learning (SRL) in first-order logic. In this paper, we propose Randomly Weighted Tensor Networks (RWTNs), which incorporate randomly drawn, untrained tensors into an NTN encoder network with a trained decoder network. We show that RWTNs meet or surpass the performance of traditionally trained LTNs for Semantic Image Interpretation (SII) tasks that have been used as a representative example of how LTNs utilize reasoning over first-order logic to exceed the performance of solely data-driven methods. We demonstrate that RWTNs outperform LTNs for the detection of the relevant part-of relations between objects, and we show that RWTNs can achieve similar performance as LTNs for object classification while using fewer parameters for learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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myAURA: Personalized health library for epilepsy management via knowledge graph sparsification and visualization

arXiv

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arXiv 2024年

作者： Correia, Rion Brattig Rozum, Jordan C. Cross, Leonard Felag, Jack Gallant, Michael Guo, Ziqi Herr, Bruce W. Min, Aehong Rocha, Deborah Stungis Wang, Xuan Börner, Katy Miller, Wendy Rocha, Luis M. Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY United States Instituto Gulbenkian de Ciência Oeiras Portugal School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Donald Bren School of Information & Computer Sciences University of California IrvineCA United States Alexander von Humboldt Fellow Technische Universität Dresden Dresden Germany Indiana University School of Nursing IndianapolisIN United States

Objective - We report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self- management. Materials and Methods - myAURA rests on the federation of an unprecedented collection of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records. A generalizable, open-source methodology was developed to compute a multi-layer knowledge graph linking all this heterogeneous data via the terms of a human-centered biomedical dictionary. Results - The power of the approach is first exemplified in the study of the drug-drug interaction phenomenon. Furthermore, we employ a novel network sparsification methodology using the metric backbone of weighted graphs, which reveals the most important edges for inference, recommendation, and visualization, such as pharmacology factors patients discuss on social media. The network sparsification approach also allows us to extract focused digital cohorts from social media whose discourse is more relevant to epilepsy or other biomedical problems. Finally, we present our patient-centered design and pilot-testing of myAURA, including its user interface, based on focus groups and other stakeholder input. Discussion - The ability to search and explore myAURA's heterogeneous data sources via a sparsified multi-layer knowledge graph, as well as the combination of those layers in a single map, are useful features for integrating relevant information for epilepsy. Conclusion - Our stakeholder-driven, scalable approach to integrate traditional and nontraditional data sources, enables biomedical discovery and data-powered patient self-management in epilepsy, and is generalizable to other chronic conditions. © 2024, CC BY.

关键词： Knowledge graph

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Data-driven probabilistic scenarios simulation model and visualization for hurricane impact

Data-driven probabilistic scenarios simulation model and vis...

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2019 Institute of Industrial and systems Engineers Annual Conference and Expo, IISE 2019

作者： Mirchandani, Pitu Ayu, Ketut Gita Maciejewski, Ross School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781713814092

The 2017 Atlantic hurricane season was horrendous and became one of the costliest hurricanes in the United States. Understanding its highly erratic movement with changing speed direction and precipitation, access to storm forecast models is essential for local and state officials and emergency responders as these models provide critical storm information (e.g., possible storm trajectories and potential impacts) that acts as basis in decision making under uncertainty for preparedness and response operations. Unfortunately, access to these sophisticated models is limited or come at substantial costs. We developed a data-driven simulation model that takes possible hurricane trajectories, referred here as scenarios, as inputs, coupled with publicly available rolling horizon forecasts of overall weather while incorporating hurricane characteristics (wind speeds, precipitation and storm surges) to generate spatial-temporal storm predicted impacts at scenario level. The model also estimates the scenario probabilities, not directly provided in the public forecasts. The computational results on hurricane Irma case study illustrates the scenario-level storm impacts are generated with fairly accurately within reasonably short runtimes. In the next phase of research not reported here, these predictions impacts can drive models for disruptions in transportation, supply chains and power grids © 2019 IISE Annual Conference and Expo 2019. All rights reserved.

关键词： Hurricanes

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Semi-supervised constrained hidden markov model using multiple sensors for remaining useful life prediction and optimal predictive maintenance 11

Semi-supervised constrained hidden markov model using multip...

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11th Annual Conference of the Prognostics and Health Management Society, PHM 2019

作者： Zhao, Xinyu Kang, Yunyi Yan, Hao Ju, Feng School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781936263295

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we propose a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is the first in the literature to simultaneously address the challenges of semisupervised setting, left-to-right constraint, and monotonicity constraint in a multiple-sensor setting. This proposed method is also effective in estimating the RUL while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method. © 2019 Prognostics and Health Management Society. All rights reserved.

关键词： NASA

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Research and Design Trends for Exam Room computing to Enhance Patient Centeredness 65th

Research and Design Trends for Exam Room Computing to Enhanc...

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65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021

作者： Saleem, Jason J. Militello, Laura G. Asan, Onur Read, Jacob M. Montague, Enid Department of Industrial Engineering University of Louisville LouisvilleKY United States Applied Decision Science LLC DaytonOH United States School of Systems & Enterprises Stevens Institute of Technology HobokenNJ United States College of Computing and Digital Media DePaul University ChicagoIL United States

ISBN: (纸本)9781713844129

While there is an extensive and established history of research that demonstrates the unfortunate capacity of exam room computing and electronic health records (EHRs) to negatively impact provider-patient communication and interaction, recent trends in exam room computing are promising in that there may be an opportunity for the EHR to improve patient engagement. The logical evolution of this research is to flip the narrative to understand strategies for using exam room computing as a mediator or facilitator of provider-patient communication and interaction, rather than simply establishing ways to mitigate the documented barriers. Panelists will present and discuss their latest research and experiences that may contribute to the evolution of using exam room computing as a tool to enhance provider-patient communication and interaction. We will also discuss how a thoughtful exam room design with patient-centered exam room computing technologies and practices may positively impact specific human factors, safety, and bias outcomes. © 2021 by Human Factors and Ergonoics Society. All rights reserved.

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Model-free hidden geometry of complex networks

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Physical Review E 2021年第1期103卷 012305-012305页

作者： Yi-Jiao Zhang Kai-Cheng Yang Filippo Radicchi Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 China Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

The fundamental idea of embedding a network in a metric space is rooted in the principle of proximity preservation. Nodes are mapped into points of the space with pairwise distance that reflects their proximity in the network. Popular methods employed in network embedding either rely on implicit approximations of the principle of proximity preservation or implement it by enforcing the geometry of the embedding space, thus hindering geometric properties that networks may spontaneously exhibit. Here we take advantage of a model-free embedding method explicitly devised for preserving pairwise proximity and characterize the geometry emerging from the mapping of several networks, both real and synthetic. We show that the learned embedding has simple and intuitive interpretations: the distance of a node from the geometric center is representative for its closeness centrality, and the relative positions of nodes reflect the community structure of the network. Proximity can be preserved in relatively low-dimensional embedding spaces, and the hidden geometry displays optimal performance in guiding greedy navigation regardless of the specific network topology. We finally show that the mapping provides a natural description of contagion processes on networks, with complex spatiotemporal patterns represented by waves propagating from the geometric center to the periphery. The findings deepen our understanding of the model-free hidden geometry of complex networks.

关键词： Clustering Guided network search Spreading

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Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

arXiv

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arXiv 2025年

作者： Kojaku, Sadamori Mahari, Robert Lera, Sandro Claudio Moro, Esteban Pentland, Alex Ahn, Yong-Yeol Department of Systems Science and Industrial Engineering Binghamton University New York United States Media Lab Massachusetts Institute of Technology Cambridge United States Harvard Law School Cambridge United States Connection Science Massachusetts Institute of Technology Cambridge United States Southern University of Science and Technology Shenzhen China Network Science Institute Department of Physics Northeastern University Boston United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes [1–3], primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems [4–6]. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization [5, 7–14], and has helped identify key mechanisms driving these patterns [5, 15, 16]. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains. © 2025, CC BY-NC-SA.

关键词： Patents and inventions

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Systematic comparison of graph embedding methods in practical tasks

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Physical Review E 2021年第4期104卷 044315-044315页

Network embedding techniques aim to represent structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the nontrivial choice of selecting the proper approach for a given application. The present work attempts to close this gap of knowledge through a systematic comparison of 11 different methods for graph embedding. We consider methods for embedding networks in the hyperbolic and Euclidean metric spaces, as well as nonmetric community-based embedding methods. We apply these methods to embed more than 100 real-world and synthetic networks. Three common downstream tasks — mapping accuracy, greedy routing, and link prediction — are considered to evaluate the quality of the various embedding methods. Our results show that some Euclidean embedding methods excel in greedy routing. As for link prediction, community-based and hyperbolic embedding methods yield an overall performance that is superior to that of Euclidean-space-based approaches. We compare the running time for different methods and further analyze the impact of different network characteristics such as degree distribution, modularity, and clustering coefficients on the quality of the embedding results. We release our evaluation framework to provide a standardized benchmark for arbitrary embedding methods.

关键词： Community structure Network navigation

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User-centered Requirements for Patient Fluid Intake Monitoring Support

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 736 - 740页

作者： Ellen J. Bass Andrew J. Abbate Yaman Noaiseh Rose Ann DiMaria-Ghalili College of Computing & InformaticsCollege of Nursing & Health Professions School of Biomedical Engineering Science & Health Systems Drexel University Philadelphia PA USA College of Computing & Informatics College of Nursing & Health Professions

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis.

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