Functional principal component analysis of glomerular filtration rate curves after kidney transplant

Dong, Jianghu; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

doi:10.1177/0962280217712088

Cited by 23 publications

(13 citation statements)

References 31 publications

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“…This leads to the following weighted least squares solutions (see for example [Gaffney, 2004], [Chamroukhi, 2013], [Faria and Soromenho, 2010]…”

Section: Maximization Stepmentioning

confidence: 99%

“…Curve clustering is the process of finding a latent class structure for observations of functional data and has wide applications across industries such as biology, finance and environmental science [Aghabozorgi et al., 2015]. Many methodologies have been developed to deal with such data [Dong et al, 2018]. These methodologies fall into four main methods; shape-based, compression based dissimilarity, feature based, and model-based clustering [Aghabozorgi et al., 2015].…”

Section: Introductionmentioning

confidence: 99%

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Route identification in the National Football League

Chu

Reyers

Thomson

et al. 2020

Journal of Quantitative Analysis in Sports

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AbstractTracking data in the National Football League (NFL) is a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. In this paper, we demonstrate how model-based curve clustering of observed player trajectories can be used to identify the routes run by eligible receivers on offensive passing plays. We use a Bernstein polynomial basis function to represent cluster centers, and the Expectation Maximization algorithm to learn the route labels for each of the 33,967 routes run on the 6963 passing plays in the data set. With few assumptions and no pre-existing labels, we are able to closely recreate the standard route tree from our algorithm. We go on to suggest ideas for new potential receiver metrics that account for receiver deployment and movement common throughout the league. The resulting route labels can also be paired with film to enable streamlined queries of game film.

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“…This leads to the following weighted least squares solutions (see for example [Gaffney, 2004], [Chamroukhi, 2013], [Faria and Soromenho, 2010]…”

Section: Maximization Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route identification in the National Football League

Chu

Reyers

Thomson

et al. 2020

Journal of Quantitative Analysis in Sports

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“…FDA is an effective technique to capture trend and variation modes in functional data (i.e. data providing information about curves, surfaces or anything else varying over a continuum), and has been widely applied in many scientific fields, including behavioural science (Rossi, Wang, and Ramsay 2002), medical research (Dong et al 2018), environmental science (Fraiman et al 2014;Lv, Fowler, and Jing 2019), etc. Furthermore, by taking Ripley's K-function analysis as an example, we also demonstrate how to apply the acquired DI values to adjust ED and correct the biased results of ED-based spatial analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the relationships between network distance and straight-line distance: applications in spatial bias correction

Chen

2021

Annals of GIS

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In many applications of spatial analysis methods, straight-line Euclidean distance (ED) is frequently used as the distance metric. However, ED is not adequate to reflect the actual distance between spatial objects and would probably lead to biased results. In order to understand the effects of using ED, this study estimates the quantitative relationships between ED and actual network distance (ND) across 25 Chinese cities and identifies their spatial variations using functional data analysis (FDA). The analysis is based on the detour index (DI), which is defined as the ratio of ND to ED. The results reveal significant linear relationships between ND and ED (with an average DI value of 1.324) across all selected cities. FDA further unveils the modes of the spatial variations of DI from short-distance to long-distance travel at the intra-city scale, showing that short-distance travels in a city usually require more detour than long-distance ones. Finally, we take K-function analysis as an example to demonstrate the usefulness of the estimated DI relationships to correct the bias of ED. Our experiments show that by applying the estimated DI relationships, the results of K-function analysis with ED can be substantially improved to become more realistic. We also suggest and evaluate a kNN based method to determine an appropriate DI value and adjust ED.

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“…FDA has often been applied in the biological and medical domains (see, for a review, [11]). Recent examples can be found in, e.g., [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

Giordani

Perna

Bianchi³

et al. 2020

PLoS ONE

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The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.

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Functional principal component analysis of glomerular filtration rate curves after kidney transplant

Cited by 23 publications

References 31 publications

Route identification in the National Football League

Route identification in the National Football League

Quantifying the relationships between network distance and straight-line distance: applications in spatial bias correction

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

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