Relevant bibliographies by topics / Joint modeling of longitudinal and survival data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Joint modeling of longitudinal and survival data'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Joint modeling of longitudinal and survival data"

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Crowther, Michael J., Keith R. Abrams, and Paul C. Lambert. "Joint Modeling of Longitudinal and Survival Data." Stata Journal: Promoting communications on statistics and Stata 13, no. 1 (March 2013): 165–84. http://dx.doi.org/10.1177/1536867x1301300112.

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Kim, Sehee, Donglin Zeng, Yi Li, and Donna Spiegelman. "Joint Modeling of Longitudinal and Cure-Survival Data." Journal of Statistical Theory and Practice 7, no. 2 (January 2013): 324–44. http://dx.doi.org/10.1080/15598608.2013.772036.

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Chen, Jia-Yuh, Richard Schulz, and Stewart J. Anderson. "Joint modeling of bivariate longitudinal and survival data in spouse pairs." Journal of Statistical Research 53, no. 1 (August 1, 2019): 1–25. http://dx.doi.org/10.47302/jsr.2019530101.

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We investigated the association between longitudinally measured depression scores and survival times simultaneously for paired spouse data from the Cardiovascular Health Study (CHS). We propose a joint model incorporating within pair correlations, both in the longitudinal and survival processes. We use bivariate linear mixed-effects models for the longitudinal processes, where the random effects are used to model the temporal correlation within each subject and the correlation across outcomes between subjects. For the survival processes, we incorporate gamma frailties into Weibull proportional hazards models to account for the correlation between survival times within pairs. The two sub-models are then linked through shared random effects, where the longitudinal and survival processes are conditionally independent given the random effects. Parameter estimates are obtained via the EM algorithm by maximizing the joint likelihood for the bivariate longitudinal and bivariate survival data. We use our method to model data where the use of bivariate longitudinal and survival sub–models are apropos but where there are no competing risks, that is, the censoring of one spouse’s time–to–mortality is not necessarily guaranteed by the death of the other spouse.
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Muniz Terrera, Graciela, Andrea M. Piccinin, Fiona Matthews, and Scott M. Hofer. "Joint Modeling of Longitudinal Change and Survival." GeroPsych 24, no. 4 (December 2011): 177–85. http://dx.doi.org/10.1024/1662-9647/a000047.

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Joint longitudinal-survival models are useful when repeated measures and event time data are available and possibly associated. The application of this joint model in aging research is relatively rare, albeit particularly useful, when there is the potential for nonrandom dropout. In this article we illustrate the method and discuss some issues that may arise when fitting joint models of this type. Using prose recall scores from the Swedish OCTO-Twin Longitudinal Study of Aging, we fitted a joint longitudinal-survival model to investigate the association between risk of mortality and individual differences in rates of change in memory. A model describing change in memory scores as following an accelerating decline trajectory and a Weibull survival model was identified as the best fitting. This model adjusted for random effects representing individual variation in initial memory performance and change in rate of decline as linking terms between the longitudinal and survival models. Memory performance and change in rate of memory decline were significant predictors of proximity to death. Joint longitudinal-survival models permit researchers to gain a better understanding of the association between change functions and risk of particular events, such as disease diagnosis or death. Careful consideration of computational issues may be required because of the complexities of joint modeling methodologies.
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Wu, Lang, Wei Liu, Grace Y. Yi, and Yangxin Huang. "Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues." Journal of Probability and Statistics 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/640153.

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In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods.
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Qiu, Feiyou, Catherine M. Stein, and Robert C. Elston. "Joint modeling of longitudinal data and discrete-time survival outcome." Statistical Methods in Medical Research 25, no. 4 (July 11, 2016): 1512–26. http://dx.doi.org/10.1177/0962280213490342.

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Hsieh, Fushing, Yi-Kuan Tseng, and Jane-Ling Wang. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited." Biometrics 62, no. 4 (April 21, 2006): 1037–43. http://dx.doi.org/10.1111/j.1541-0420.2006.00570.x.

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Martins, Rui, Giovani L. Silva, and Valeska Andreozzi. "Bayesian joint modeling of longitudinal and spatial survival AIDS data." Statistics in Medicine 35, no. 19 (March 14, 2016): 3368–84. http://dx.doi.org/10.1002/sim.6937.

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Hwang, Yi-Ting, Chia-Hui Huang, Chun-Chao Wang, Tzu-Yin Lin, and Yi-Kuan Tseng. "Joint modelling of longitudinal binary data and survival data." Journal of Applied Statistics 46, no. 13 (March 19, 2019): 2357–71. http://dx.doi.org/10.1080/02664763.2019.1590540.

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Liu, Lei. "Joint modeling longitudinal semi-continuous data and survival, with application to longitudinal medical cost data." Statistics in Medicine 28, no. 6 (November 28, 2008): 972–86. http://dx.doi.org/10.1002/sim.3497.

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Dissertations / Theses on the topic "Joint modeling of longitudinal and survival data"

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Pericleous, Paraskevi. "Parametric joint modelling for longitudinal and survival data." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59673/.

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Joint modelling is the simultaneous modelling of longitudinal and survival data, while taking into account a possible association between them. A common approach in joint modelling studies is to assume that the repeated measurements follow a lin- ear mixed e�ects model and the survival data is modelled using a Cox proportional hazards model. The Cox model, however, requires a strong proportionality assump- tion, which seems to be violated quite often. We, thus, propose the use of parametric survival models. Additionally, joint modelling literature mainly deals with right- censoring only and does not consider left-truncation, which can cause bias. The joint model proposed here considers left-truncation and right-censoring.
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Rajeev, Deepthi. "Separate and Joint Analysis of Longitudinal and Survival Data." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1775.pdf.

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Li, Qiuju. "Statistical inference for joint modelling of longitudinal and survival data." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.

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In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.
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Qiu, Feiyou. "JOINT MODELING OF LONGITUDINAL DATA AND DISCRETE-TIME SURVIVAL OUTCOME WITH APPLICATION TO STUDYING TUBERCULOSIS IMMUNOLOGY DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1322846245.

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VanderWyden, Piccorelli Annalisa. "Joint Modeling the Relationship between Longitudinal and Survival Data Subject to Left Truncation with Applications to Cystic Fibrosis." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1283437365.

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Lourens, Spencer. "Bias in mixtures of normal distributions and joint modeling of longitudinal and time-to-event data with monotonic change curves." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1685.

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Estimating parameters in a mixture of normal distributions dates back to the 19th century when Pearson originally considered data of crabs from the Bay of Naples. Since then, many real world applications of mixtures have led to various proposed methods for studying similar problems. Among them, maximum likelihood estimation (MLE) and the continuous empirical characteristic function (CECF) methods have drawn the most attention. However, the performance of these competing estimation methods has not been thoroughly studied in the literature and conclusions have not been consistent in published research. In this article, we review this classical problem with a focus on estimation bias. An extensive simulation study is conducted to compare the estimation bias between the MLE and CECF methods over a wide range of disparity values. We use the overlapping coefficient (OVL) to measure the amount of disparity, and provide a practical guideline for estimation quality in mixtures of normal distributions. Application to an ongoing multi-site Huntington disease study is illustrated for ascertaining cognitive biomarkers of disease progression. We also study joint modeling of longitudinal and time-to-event data and discuss pattern-mixture and selection models, but focus on shared parameter models, which utilize unobserved random effects in order to "join" a marginal longitudinal data model and marginal survival model in order to assess an internal time-dependent covariate's effect on time-to-event. The marginal models used in the analysis are the Cox Proportional Hazards model and the Linear Mixed model, and both of these models are covered in some detail before defining joints models and describing the estimation process. Joint modeling provides a modeling framework which accounts for correlation between the longitudinal data and the time-to-event data, while also accounting for measurement error in the longitudinal process, which previous methods failed to do. Since it has been shown that bias is incurred, and this bias is proportional to the amount of measurement error, utilizing a joint modeling approach is preferred. Our setting is also complicated by monotone degeneration of the internal covariate considered, and so a joint model which utilizes monotone B-Splines to recover the longitudinal trajectory and a Cox Proportional Hazards (CPH) model for the time-to-event data is proposed. The monotonicity constraints are satisfied via the Projected Newton Raphson Algorithm as described by Cheng et al., 2012, with the baseline hazard profiled out of the $Q$ function in each M-step of the Expectation Maximization (EM) algorithm used for optimizing the observed likelihood. This method is applied to assess Total Motor Score's (TMS) ability to predict Huntington Disease motor diagnosis in the Biological Predictors of Huntington's Disease study (PREDICT-HD) data.
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McCrink, L. M. "Outlier effects on robust joint modelling of longitudinal and survival date." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679256.

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Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their detrimental impact being down weighed and thus providing more efficient and reliable estimates. The robust joint modelling technique and its major benefits are showcased through the analysis of Northern Irish end stage renal disease patients. With an ageing population and growing prevalence of chronic kidney disease within the United Kingdom, there is a pressing demand to investigate the detrimental relationship between the changing haemoglobin levels of haemodialysis patients and their survival. As outliers within the NI renal data were found to have significantly worse survival, identification of outlying individuals through robust joint modelling may aid nephrologists to improve patient's survival. A simulation study was also undertaken to explore the difference between robust and standard joint models in the presence of increasing proportions and extremity of longitudinal outliers. More efficient and reliable estimates were obtained by robust joint models with increasing contrast between the robust and standard joint models when a greater proportion of more extreme outliers are present. Through illustration of the gains in efficiency and reliability of parameters when outliers exist, the potential of robust joint modelling is evident. The research presented in this thesis highlights the benefits and stresses the need to utilise a more robust approach to joint modelling in the presence of longitudinal outliers.
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Rajeswaran, Jeevanantham. "JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354508776.

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Wang, Xu. "Joint inference for longitudinal and survival data with incomplete time-dependent covariates." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27842.

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In many longitudinal studies, individual characteristics associated with their repeated measures may be covariates for the time to an event of interest. Thus, it is desirable to model both the survival process and the longitudinal process together. Statistical analysis may be complicated with missing data or measurement errors in the time-dependent covariates. This thesis considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the survival process. We provide a method based on the joint likelihood for nonignorable missing data, and we extend the method to the case of time-dependent covariates. We adapt a Monte Carlo EM algorithm to estimate the model parameters. We compare the method with the existing two-step method with some interesting findings. A real example from a recent HIV study is used as an illustration.
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Crowther, Michael James. "Development and application of methodology for the parametric analysis of complex survival and joint longitudinal-survival data in biomedical research." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/31597.

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The occurrence of survival, or time-to-event, data is commonplace in medical research, where interest lies in the time it takes from a given baseline, for an event of interest to occur, and the factors that are associated with it. For example, this could be the effect of a treatment on the time to death since diagnosis of cardiovascular disease. The primary aim of this thesis is to develop parametric methods for the analysis of complex survival data, including the extension to joint models of longitudinal and survival data, to provide a number of advantages over the commonly used semi-parametric Cox model. New and current methodology is often assessed using simulation studies; however, often in the field of survival analysis they are simplistic and fail to reflect biologically plausible scenarios. In this thesis a general algorithm for simulating complex survival data, from any given hazard function, is proposed and assessed. A general framework for the parametric analysis of survival data is then developed, utilising numerical quadrature, illustrated in detail using the special case of restricted cubic splines to model the baseline hazard and time-dependent effects. Extensions to the framework including cluster robust standard errors and excess mortality models are also considered. Finally, the joint longitudinal-survival modelling framework is extended to incorporate the Royston- Parmar survival model, and a mixture of two parametric distributions, both evaluated through simulation, utilising the proposed simulation algorithm, showing advantages over more simple parametric approaches. The estimation of joint models, using Gaussian quadrature, is also evaluated through an extensive simulation study. Throughout the thesis, user friendly software is developed to implement the methodological components, allowing statisticians and non-statisticians alike, to apply the methods directly. A variety of clinical datasets in the areas of cancer, cardiovascular disease and liver cirrhosis are used to exemplify the proposals.
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Books on the topic "Joint modeling of longitudinal and survival data"

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Li, Gang, Ning Li, and Robert M. Elashoff. Joint Modeling of Longitudinal and Time-To-event Data. Taylor & Francis Group, 2020.

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Elashoff, Robert. Joint Modeling of Longitudinal and Time-to-Event Data. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/9781315374871.

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Book chapters on the topic "Joint modeling of longitudinal and survival data"

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Dupuy, Jean-François. "Joint Modeling of Survival and Nonignorable Missing Longitudinal Quality-of-Life Data." In Statistical Methods for Quality of Life Studies, 309–22. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3625-0_25.

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Guler, Ipek, Christel Faes, Francisco Gude, and Carmen Cadarso-Suárez. "Recent Developments and Advances in Joint Modelling of Longitudinal and Survival Data." In Studies in Systems, Decision and Control, 219–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73848-2_21.

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Song, Hui, Yingwei Peng, and Dongsheng Tu. "Recent Development in the Joint Modeling of Longitudinal Quality of Life Measurements and Survival Data from Cancer Clinical Trials." In Advanced Statistical Methods in Data Science, 153–68. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5_8.

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Guler, Ipek, Christel Faes, Carmen Cadarso-Suárez, and Francisco Gude. "Joint Modelling for Flexible Multivariate Longitudinal and Survival Data: Application in Orthotopic Liver Transplantation." In Trends in Mathematics, 35–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55639-0_6.

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Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Joint Models for Longitudinal and Survival Data." In Bayesian Survival Analysis, 262–89. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_7.

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Li, Yang, Xin He, Haiying Wang, and Jianguo Sun. "Joint Analysis of Longitudinal Data and Informative Observation Times with Time-Dependent Random Effects." In New Developments in Statistical Modeling, Inference and Application, 37–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42571-9_2.

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Rizopoulos, Dimitris. "Joint Modeling of Longitudinal and Time-to-Event Data: Challenges and Future Directions." In Advances in Theoretical and Applied Statistics, 199–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35588-2_19.

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Vardhan, Avantika, Marcel Prastawa, Neda Sadeghi, Clement Vachet, Joseph Piven, and Guido Gerig. "Joint Longitudinal Modeling of Brain Appearance in Multimodal MRI for the Characterization of Early Brain Developmental Processes." In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, 49–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14905-9_5.

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Zhang, Ningshan, and Jeffrey S. Simonoff. "The Potential for Nonparametric Joint Latent Class Modeling of Longitudinal and Time-to-Event Data." In Springer Proceedings in Mathematics & Statistics, 525–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57306-5_47.

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"Joint Modeling Longitudinal Data and Survival Data." In Mixed Effects Models for Complex Data, 253–92. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420074086-c8.

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Conference papers on the topic "Joint modeling of longitudinal and survival data"

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Borges, Ana, Inês Sousa, and Luis Castro. "Joint modelling of longitudinal CEA tumour marker progression and survival data on breast cancer." In APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science. Author(s), 2017. http://dx.doi.org/10.1063/1.4981983.

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Shiao, Han-Tai, and Vladimir Cherkassky. "Learning using privileged information (LUPI) for modeling survival data." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889517.

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Tong, Xiao, James Dunyak, Diansong Zhou, David Carlile, Helen Tomkinson, Gabriel Helmlinger, Nidal Al-Huniti, and Hongmei Xu. "Abstract 4760: Joint modeling of longitudinal tumor dynamics and survival in non-small cell lung cancer (NSCLC) patients." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-4760.

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Biganzoli, Elia M., Federico Ambrogi, and Patrizia Boracchi. "Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178824.

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"The analysis of different longitudinal biomarkers association with the overall survival in non-small cell lung cancer by means of joint modeling." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-324.

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Sperry, Brian, Corina Sandu, and Brent Ballew. "Complex Bogie Modeling Incorporating Advanced Friction Wedge Components." In 2009 Joint Rail Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/jrc2009-63037.

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This research focuses on the dynamic behavior of the three-piece bogie that supports the freight train car bodies. While the system is relatively simple, in that there are very few parts involved, the behavior of the bogie is somewhat more complex. Our research focuses primarily on the behavior of the friction wedges under different operating conditions that are seen under normal operation. The Railway Technologies Laboratory (RTL) at Virginia Tech has been developing a model to better capture the dynamic behavior of friction wedges using 3-D modeling software. In previous years, a quarter-truck model, and half-truck variably damped model have been developed using MathWorks MATLAB®. This year, research has focused on the development of a half-truck variably damped model with a new (curved surface) friction wedge, and a half-truck constantly damped model, both using the MATLAB® based software program. Currently a full-truck variably damped model has been created using LMS Virtual.Lab. This software allows for a model that is more easily created and modified, as well as allowing for a much shorter simulation time, which became a necessity as more contact points, and more complex inputs were needed to increase the accuracy of the simulation results. The new model consists of seven rigid bodies: the bolster, two sideframes, and four wedges. We have also implemented full spring nests on each sideframe, where in previous models equivalent spring forces were used. The model allows six degrees-of-freedom for the wedges and bolster: lateral, longitudinal, and vertical translations, as well as pitch, roll, and yaw. The sideframes are constrained to two degrees-of-freedom: vertical and longitudinal translations. The inputs to the model are vertical and longitudinal translations or forces on the sideframes, which can be set completely independent of each other. The model simulation results have been compared with results from NUCARS®, an industrially-used train modeling software developed by the Transportation Technology Center, Inc. (TTCI), a wholly owned subsidiary of the Association of American Railroads (AAR), for similar inputs, as well as experimental data from warping tests performed at TTCI.
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Yang, Xi, Yuan Zhang, and Min Chi. "Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/493.

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Electronic healthcare records (EHRs) are comprehensive longitudinal collections of patient data that play a critical role in modeling the disease progression to facilitate clinical decision-making. Based on EHRs, in this work, we focus on sepsis -- a broad syndrome that can develop from nearly all types of infections (e.g., influenza, pneumonia). The symptoms of sepsis, such as elevated heart rate, fever, and shortness of breath, are vague and common to other illnesses, making the modeling of its progression extremely challenging. Motivated by the recent success of a novel subsequence clustering approach: Toeplitz Inverse Covariance-based Clustering (TICC), we model the sepsis progression as a subsequence partitioning problem and propose a Multi-series Time-aware TICC (MT-TICC), which incorporates multi-series nature and irregular time intervals of EHRs. The effectiveness of MT-TICC is first validated via a case study using a real-world hand gesture dataset with ground-truth labels. Then we further apply it for sepsis progression modeling using EHRs. The results suggest that MT-TICC can significantly outperform competitive baseline models, including the TICC. More importantly, it unveils interpretable patterns, which sheds some light on better understanding the sepsis progression.
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Nourpanah, Nikzad, and Farid Taheri. "Finite Element Analysis of Strain Concentration in Field Joint of Concrete Coated Pipelines." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-79047.

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Realistic and accurate modeling of the strains developed in concrete coated pipelines is an important objective to offshore pipeline industry. This is because of the acceptance of the strain-based design methods and also the increasing demand on pipelines to operate under harsher environments/loading conditions. The problem has several sources of nonlinearity, namely: material plasticity, concrete cracking and crushing and concrete slippage on the steel pipe. In this paper, a framework and procedure for finite element (FE) modeling of concrete coated pipelines is presented and verified against test results available in literature. The mechanics of strain concentration at the Field Joint (FJ), where the coating has an abrupt discontinuity is described and studied via the verified FE model. These aspects are all described and modeled appropriately using the general purpose FE software ABAQUS, resulting in a realistic and accurate FE model which predicts the strain and stress distribution in the steel, concrete coating and the anticorrosion layer. Output results, presented in the form of variation of moment versus strain, longitudinal distribution of the axial strains, the maximum FJ strains, strain concentration factor as a function of global strain and relative slippage of concrete coating are reported and verified with comparison to test data. Good agreements, both in trend and also quantities are observed, thereby verifying the integrity of the framework suited for the further development, which would include a parametric study with the aim of developing practical design equations. Discussion on the circumferential distribution of shear stresses in the anticorrosion layer is also presented. FE results show a constant shear stress distributed nearly all along the circumference, in concert with the test results.
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Xu, Wei, Zhenjia (Jerry) Huang, and Hyunjoe Kim. "Thorough Verification and Validation of CFD Prediction of FPSO Current Load for Confident Applications." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95017.

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Abstract:
Abstract In this paper, a thorough verification of FPSO current load modeling and simulation using CFD was carried out and a modeling practice developed in a joint development project [1] was adopted. The towing test data obtained with thorough quality assurance process were used as benchmark data in the verification work. To have high confidence in the CFD modeling and simulations, both steady simulations with RANS model and unsteady simulations with IDDES model were carried out. For the steady simulations, sensitivity checks were carried out for the domain size, mesh refinement, turbulence models, boundary conditions and Reynolds effect. For unsteady simulations, the wake zone mesh refinement, time step size, number of inner iterations and different RANS model for boundary layers were considered during the sensitivity verification stage. It was found in this study that the transverse load (Fy) and yaw moment (Mz) of the FPSO can be predicted fairly well using RANS model, while the DES model needs to be adopted in order to accurately predict the longitudinal forces (Fx) at certain range of current directions. The wake grid for the DES needs to be fine enough in order to capture the details of vortices and the running time trace needs to be long enough to reduce the sensitivity on the mean current forces.
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