Auswahl der wissenschaftlichen Literatur zum Thema „Disease progression modeling“
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Zeitschriftenartikel zum Thema "Disease progression modeling"
Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry und Wei Xiao. „Rheumatoid Arthritis Disease Progression Modeling“. Therapeutic Innovation & Regulatory Science 47, Nr. 6 (November 2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.
Der volle Inhalt der QuelleInoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell und Peter Müller. „Modeling Disease Progression With Longitudinal Markers“. Journal of the American Statistical Association 103, Nr. 481 (01.03.2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.
Der volle Inhalt der QuellePlevritis, Sylvia K. „Modeling disease progression in outcomes research“. Academic Radiology 6 (Januar 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.
Der volle Inhalt der QuelleYoung, Alexandra L., Felix J. S. Bragman, Bojidar Rangelov, MeiLan K. Han, Craig J. Galbán, David A. Lynch, David J. Hawkes et al. „Disease Progression Modeling in Chronic Obstructive Pulmonary Disease“. American Journal of Respiratory and Critical Care Medicine 201, Nr. 3 (01.02.2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.
Der volle Inhalt der QuelleRooney, William D., Yosef A. Berlow, William T. Triplett, Sean C. Forbes, Rebecca J. Willcocks, Dah-Jyuu Wang, Ishu Arpan et al. „Modeling disease trajectory in Duchenne muscular dystrophy“. Neurology 94, Nr. 15 (17.03.2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.
Der volle Inhalt der QuelleZhou, Jiayu, Jun Liu, Vaibhav A. Narayan und Jieping Ye. „Modeling disease progression via multi-task learning“. NeuroImage 78 (September 2013): 233–48. http://dx.doi.org/10.1016/j.neuroimage.2013.03.073.
Der volle Inhalt der QuelleMehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin und Lauge Sørensen. „Robust parametric modeling of Alzheimer’s disease progression“. NeuroImage 225 (Januar 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.
Der volle Inhalt der QuelleSun, Zhaonan, Soumya Ghosh, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio und Jianying Hu. „A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data“. JAMIA Open 2, Nr. 1 (07.01.2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.
Der volle Inhalt der QuelleGomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin und Michael Gold. „Modeling Alzheimer's disease progression using the disease system analysis approach“. Alzheimer's & Dementia 8, Nr. 1 (22.07.2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.
Der volle Inhalt der QuelleCook, Sarah F., und Robert R. Bies. „Disease Progression Modeling: Key Concepts and Recent Developments“. Current Pharmacology Reports 2, Nr. 5 (15.08.2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.
Der volle Inhalt der QuelleDissertationen zum Thema "Disease progression modeling"
Huszti, Ella. „Markov modeling of disease progression and mortality“. Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95060.
Der volle Inhalt der QuelleLes études pronostiques sur l'évolution et la mortalité de certaines pathologies sont essentielles pour comprendre le rôle de certains facteurs pronostiques et ainsi, améliorer le pronostic et finalement aider dans le choix des interventions thérapeutiques appropriées. Jusqu'à présent, les études de ce type ont été souvent confrontées à d'importantes limitations dans les données et/ou les méthodes statistiques disponibles. Une des difficultés concerne la discrimination, pour un même facteur pronostique, de ses effets sur différents critères cliniques ou événements concurrents, comme la récidive de la maladie vs le décès sans récidive, ou le décès dû à la pathologie vs le décès dû à d'autres causes. Ce problème devient d'autant plus important que les sources de données, comme les registres, enregistrent souvent uniquement la date de décès mais pas la cause. Ceci peut conduire à des biais dans l'évaluation du rôle des facteurs pronostiques dont l'effet sur la mortalité spécifique dû à la pathologie est différent de celui sur la mortalité toutes causes confondues. Il est donc important d'utiliser des méthodes qui puissent prendre en compte correctement à la fois (i) les différentes évolutions possibles de la pathologie et (ii) l'absence de la connaissance de la cause de décès. Les problèmes méthodologiques mentionnés précédemment sont traités dans 3 articles. Les études empiriques précédentes ont suggéré des avantages potentiels à utiliser les modèles multi-états de Markov à la place des modèles de survie conventionnels dans l'analyse des risques compétitifs et des différentes phases possibles d'évolution d'une pathologie. Le premier article tente d'évaluer méthodiquement, à l'aide de simulations, les performances des modèles de Markov dans ce contexte et confirme l'exactitude à la fois de l'estimation des coefficients de la régression et des tests d'hypothèse. D'un autre coté, les mod
Sauty, Benoît. „Multimodal modelling of Alzheimer's Disease progression“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS348.
Der volle Inhalt der QuelleAlzheimer's disease (AD) is a multi-facet pathology, that can be monitored through a variety of data types. This thesis aims to leverage multimodal longitudinal data, especially imaging scans and cognitive tests, to provide a statistical description of the progression of AD and to enable individual forecasting of future decline. Mixed-effect disease progression models (DPMs) are commonly used for these tasks. In this context, our first contribution questions the frequent assumption that biomarkers follow linear or logistic functions over time, and we propose a geometric framework that assumes the data lie on a manifold and follow geodesics over time. We learn the Riemannian metric of the observation space and are able to model a wider variety of biomarkers, without priors on the shape of the trajectory over time. Using variational auto-encoders, we then extend this framework to neuroimaging data (MRI or PET scans), in order to provide high-dimensional progression models that describe the patterns of structural and functional alterations of the brain over the course of AD. We then apply this family of DPMs to clinical studies data in order to investigate the heterogeneity of AD progression, due to APOE-e4 genotype and sex on patterns of brain alterations. Lastly, we use said DPMs with a set of imaging and fluid biomarkers to identify the specific combinations of input features that best forecast cognitive declines in patients at different stages of the disease. The thesis demonstrates that DPMs can effectively model the progression of AD using a great variety of multimodal longitudinal data and provide valuable insights into the disease's clinical manifestations and progression. These findings can inform clinical trial design and facilitate more accurate prognosis and individualized treatment strategies for patients with AD
McHugh, Kevin J. „Age-related macular degeneration: interventional tissue engineering and predictive modeling of disease progression“. Thesis, Boston University, 2014. https://hdl.handle.net/2144/19690.
Der volle Inhalt der QuelleAge-related macular degeneration (AMD) is the leading cause of irreversible blindness in people over the age of 50. As many as 50 million people are affected by AMD worldwide and prevalence is expected to continue to rise due to an aging population. There are two forms of the disease, dry (geographic atrophy) and wet (choroidal neovascularization), both of which result in retinal degeneration and central vision loss. Although anti-vascular endothelial growth factor therapies are moderately successful at treating the wet form, there are no treatments currently available for the more common dry form. Pharmacological therapies have been extensively explored for the treatment of dry AMD, but have achieved little success because the pathogenesis underlying AMD is unknown and likely varies among patients . Recently, tissue engineering has emerged as a promising approach to restore function by replacing diseased retinal tissue with healthy retinal pigment epithelium (RPE). While AMD-associated vision loss occurs when photoreceptors degenerate, this process arises as a consequence of earlier RPE dysfunction. In the healthy retina, the RPE acts as a critical regulator of the microenvironment for both photoreceptors and the nearby vasculature. However in AMD, the RPE no longer performs these essential homeostatic functions leading to photoreceptor apoptosis and vision loss. This dissertation describes the development and in vitro characterization of a tissue engineering scaffold for RPE delivery as potential treatment for dry AMD. First, a novel microfabrication-based method termed "pore casting" was developed to produce thin scaffolds with highly controlled pore size, shape, and spacing. Next, human RPE were cultured on pore-cast poly(c-caprolactone) (PCL) scaffolds and compared to cells on track-etched polyester, the standard RPE culture substrate. RPE on porous PCL demonstrated enhanced maturation and function compared to track-etched polyester including improved pigmentation, barrier formation, gene expression, growth factor secretion, and phagocytic degradation. Lastly, this study established a patient-specific method for predicting AMD progression using retinal oxygen concentration. This approach differs from current diagnosis techniques because it uses physiologically-relevant mechanisms rather than generalized clinical associations which have little, if any, prognostic value.
Shelton, Morgan Griffin. „Modeling the Effects of Supercomplex Formation and Stress Response on Alzheimer’S Disease Progression“. W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1563899025.
Der volle Inhalt der QuelleConrado, Daniela J., Timothy Nicholas, Kuenhi Tsai, Sreeraj Macha, Vikram Sinha, Julie Stone, Brian Corrigan et al. „Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials: A Disease Progression Modeling Analysis“. WILEY, 2018. http://hdl.handle.net/10150/626602.
Der volle Inhalt der QuelleHubbard, Rebecca Allana. „Modeling a non-homogeneous Markov process via time transformation /“. Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9607.
Der volle Inhalt der QuelleBône, Alexandre. „Learning adapted coordinate systems for the statistical analysis of anatomical shapes. Applications to Alzheimer's disease progression modeling“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS273.
Der volle Inhalt der QuelleThis thesis aims to build coordinate systems for shapes i.e. finite-dimensional metric spaces where shapes are represented by vectors. The goal of building such coordinate systems is to allow and facilitate the statistical analysis of shape data sets. The end-game motivation of our work is to predict and sub-type Alzheimer’s disease, based in part on knowledge extracted from banks of brain medical images. Even if these data banks are longitudinal, their variability remains mostly due to the large and normal inter-individual variability of the brain. The variability due to the progression of pathological alterations is of much smaller amplitude. The central objective of this thesis is to develop a coordinate system adapted for the statistical analysis of longitudinal shape data sets, able to disentangle these two sources of variability. As shown in the literature, the parallel transport operator can be leveraged to achieve this desired disentanglement, for instance by defining the notion of exp-parallel curves on a manifold. Using this tool on shape spaces comes however with theoretical and computational challenges, tackled in the first part of this thesis. Finally, if shape spaces are commonly equipped with a manifold-like structure in the field of computational anatomy, the underlying classes of diffeomorphisms are however most often largely built and parameterized without taking into account the data at hand. The last major objective of this thesis is to build deformation-based coordinate systems where the parameterization of deformations is adapted to the data set of interest
Robertson, Chadia L. „Analysis of the Role of Astrocyte Elevated Gene-1 in Normal Liver Physiology and in the Onset and Progression of Hepatocellular Carcinoma“. VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3573.
Der volle Inhalt der QuelledePillis-Lindheim, Lydia. „Disease Correlation Model: Application to Cataract Incidence in the Presence of Diabetes“. Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/294.
Der volle Inhalt der QuelleLaranjeira, Simão. „Modelling the progression of neurodegenerative diseases“. Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:ebb621d0-e4e6-405e-9e54-ba385c3ebd0a.
Der volle Inhalt der QuelleBücher zum Thema "Disease progression modeling"
Aspden, Richard, und Jenny Gregory. Morphology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0011.
Der volle Inhalt der QuelleBuchteile zum Thema "Disease progression modeling"
Camargo, Anyela, und Jan T. Kim. „Disease Progression Modeling“. In Encyclopedia of Systems Biology, 582. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_221.
Der volle Inhalt der QuelleIbarra, Manuel, Marianela Lorier und Iñaki F. Trocóniz. „Pharmacometrics: Disease Progression Modeling“. In The ADME Encyclopedia, 939–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84860-6_174.
Der volle Inhalt der QuelleIbarra, Manuel, Marianela Lorier und Iñaki F. Trocóniz. „Pharmacometrics: Disease Progression Modeling“. In The ADME Encyclopedia, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_174-1.
Der volle Inhalt der QuelleNg, Kenney, Mohamed Ghalwash, Prithwish Chakraborty, Daby M. Sow, Akira Koseki, Hiroki Yanagisawa und Michiharu Kudo. „Data-Driven Disease Progression Modeling“. In Health Informatics, 247–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07912-2_17.
Der volle Inhalt der QuelleMould, Diane R. „Modeling the Progression of Disease“. In Pharmacokinetics in Drug Development, 57–90. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7937-7_3.
Der volle Inhalt der QuelleOxtoby, Neil P. „Data-Driven Disease Progression Modeling“. In Machine Learning for Brain Disorders, 511–32. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_17.
Der volle Inhalt der QuelleSelf, Steve, und Yudi Pawitan. „Modeling a Marker of Disease Progression and Onset of Disease“. In AIDS Epidemiology, 231–55. Boston, MA: Birkhäuser Boston, 1992. http://dx.doi.org/10.1007/978-1-4757-1229-2_11.
Der volle Inhalt der QuelleVenkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen und Stefan Klein. „A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling“. In Lecture Notes in Computer Science, 121–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59050-9_10.
Der volle Inhalt der QuelleLey-Chavez, Adriana, und Julia L. Higle. „MODELING DISEASE PROGRESSION AND RISK-DIFFERENTIATED SCREENING FOR CERVICAL CANCER PREVENTION“. In Decision Analytics and Optimization in Disease Prevention and Treatment, 153–82. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781118960158.ch7.
Der volle Inhalt der QuelleRivail, Antoine, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu und Hrvoje Bogunovic. „Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning“. In Predictive Intelligence in Medicine, 44–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6_5.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Disease progression modeling"
Yang, Xi, Ge Gao und Min Chi. „Hierarchical Apprenticeship Learning for Disease Progression Modeling“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/265.
Der volle Inhalt der QuelleWang, Xulong, Jun Qi, Yun Yang und Po Yang. „A Survey of Disease Progression Modeling Techniques for Alzheimer's Diseases“. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). IEEE, 2019. http://dx.doi.org/10.1109/indin41052.2019.8972091.
Der volle Inhalt der QuellePearson, Ronald K., Robert J. Kingan und Alan Hochberg. „Disease progression modeling from historical clinical databases“. In Proceeding of the eleventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1081870.1081974.
Der volle Inhalt der QuelleSukkar, R., E. Katz, Yanwei Zhang, D. Raunig und B. T. Wyman. „Disease progression modeling using Hidden Markov Models“. In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346556.
Der volle Inhalt der QuelleLiu, Xiaoli, Jiali Li und Peng Cao. „Modeling Disease Progression with Deep Neural Networks“. In ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3451421.3451429.
Der volle Inhalt der QuelleZhou, Jiayu, Jun Liu, Vaibhav A. Narayan und Jieping Ye. „Modeling disease progression via fused sparse group lasso“. In the 18th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339702.
Der volle Inhalt der QuelleZheng, Kaiping, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi und Wei Luen James Yip. „Capturing Feature-Level Irregularity in Disease Progression Modeling“. In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3132944.
Der volle Inhalt der QuelleJeong, Seungwoo, Wonsik Jung, Junghyo Sohn und Heung-Il Suk. „Deep Geometrical Learning for Alzheimer’s Disease Progression Modeling“. In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00031.
Der volle Inhalt der QuelleRoberts, Michael D., Ian A. Sigal, Yi Liang, Claude F. Burgoyne und J. Crawford Downs. „Finite Element Modeling of the Connective Tissues of the Optic Nerve Head in Bilaterally Normal Monkeys“. In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206522.
Der volle Inhalt der QuelleZhou, Menghui, Xulong Wang, Yun Yang, Fengtao Nan, Yu Zhang, Jun Qi und Po Yang. „Modeling Disease Progression Flexibly with Nonlinear Disease Structure via Multi-task Learning“. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2021. http://dx.doi.org/10.1109/msn53354.2021.00063.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Disease progression modeling"
Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, Mai 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.
Der volle Inhalt der QuelleRuvinsky, Alicia, Maria Seale, R. Salter und Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), März 2023. http://dx.doi.org/10.21079/11681/46632.
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