Literatura científica selecionada sobre o tema "Disease progression modeling"
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Artigos de revistas sobre o assunto "Disease progression modeling"
Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry e Wei Xiao. "Rheumatoid Arthritis Disease Progression Modeling". Therapeutic Innovation & Regulatory Science 47, n.º 6 (novembro de 2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.
Texto completo da fonteInoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell e Peter Müller. "Modeling Disease Progression With Longitudinal Markers". Journal of the American Statistical Association 103, n.º 481 (1 de março de 2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.
Texto completo da fontePlevritis, Sylvia K. "Modeling disease progression in outcomes research". Academic Radiology 6 (janeiro de 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.
Texto completo da fonteYoung, 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, n.º 3 (1 de fevereiro de 2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.
Texto completo da fonteRooney, 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, n.º 15 (17 de março de 2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.
Texto completo da fonteZhou, Jiayu, Jun Liu, Vaibhav A. Narayan e Jieping Ye. "Modeling disease progression via multi-task learning". NeuroImage 78 (setembro de 2013): 233–48. http://dx.doi.org/10.1016/j.neuroimage.2013.03.073.
Texto completo da fonteMehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin e Lauge Sørensen. "Robust parametric modeling of Alzheimer’s disease progression". NeuroImage 225 (janeiro de 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.
Texto completo da fonteSun, Zhaonan, Soumya Ghosh, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio e Jianying Hu. "A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data". JAMIA Open 2, n.º 1 (7 de janeiro de 2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.
Texto completo da fonteGomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin e Michael Gold. "Modeling Alzheimer's disease progression using the disease system analysis approach". Alzheimer's & Dementia 8, n.º 1 (22 de julho de 2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.
Texto completo da fonteCook, Sarah F., e Robert R. Bies. "Disease Progression Modeling: Key Concepts and Recent Developments". Current Pharmacology Reports 2, n.º 5 (15 de agosto de 2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteLes 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.
Texto completo da fonteAlzheimer'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.
Texto completo da fonteAge-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.
Texto completo da fonteConrado, 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.
Texto completo da fonteHubbard, 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.
Texto completo da fonteBône, 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.
Texto completo da fonteThis 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.
Texto completo da fontedePillis-Lindheim, Lydia. "Disease Correlation Model: Application to Cataract Incidence in the Presence of Diabetes". Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/294.
Texto completo da fonteLaranjeira, Simão. "Modelling the progression of neurodegenerative diseases". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:ebb621d0-e4e6-405e-9e54-ba385c3ebd0a.
Texto completo da fonteLivros sobre o assunto "Disease progression modeling"
Aspden, Richard, e Jenny Gregory. Morphology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0011.
Texto completo da fonteCapítulos de livros sobre o assunto "Disease progression modeling"
Camargo, Anyela, e 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.
Texto completo da fonteIbarra, Manuel, Marianela Lorier e 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.
Texto completo da fonteIbarra, Manuel, Marianela Lorier e 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.
Texto completo da fonteNg, Kenney, Mohamed Ghalwash, Prithwish Chakraborty, Daby M. Sow, Akira Koseki, Hiroki Yanagisawa e 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.
Texto completo da fonteMould, 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.
Texto completo da fonteOxtoby, 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.
Texto completo da fonteSelf, Steve, e 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.
Texto completo da fonteVenkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen e 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.
Texto completo da fonteLey-Chavez, Adriana, e 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.
Texto completo da fonteRivail, Antoine, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Disease progression modeling"
Yang, Xi, Ge Gao e 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.
Texto completo da fonteWang, Xulong, Jun Qi, Yun Yang e 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.
Texto completo da fontePearson, Ronald K., Robert J. Kingan e 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.
Texto completo da fonteSukkar, R., E. Katz, Yanwei Zhang, D. Raunig e 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.
Texto completo da fonteLiu, Xiaoli, Jiali Li e 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.
Texto completo da fonteZhou, Jiayu, Jun Liu, Vaibhav A. Narayan e 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.
Texto completo da fonteZheng, Kaiping, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi e 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.
Texto completo da fonteJeong, Seungwoo, Wonsik Jung, Junghyo Sohn e 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.
Texto completo da fonteRoberts, Michael D., Ian A. Sigal, Yi Liang, Claude F. Burgoyne e 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.
Texto completo da fonteZhou, Menghui, Xulong Wang, Yun Yang, Fengtao Nan, Yu Zhang, Jun Qi e 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Disease progression modeling"
Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, maio de 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.
Texto completo da fonteRuvinsky, Alicia, Maria Seale, R. Salter e Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), março de 2023. http://dx.doi.org/10.21079/11681/46632.
Texto completo da fonte