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Artículos de revistas sobre el tema "Disease progression modeling"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry, and Wei Xiao. "Rheumatoid Arthritis Disease Progression Modeling." Therapeutic Innovation & Regulatory Science 47, no. 6 (2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.

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2

Inoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell, and Peter Müller. "Modeling Disease Progression With Longitudinal Markers." Journal of the American Statistical Association 103, no. 481 (2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.

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3

Plevritis, Sylvia K. "Modeling disease progression in outcomes research." Academic Radiology 6 (January 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.

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4

Young, Alexandra L., Felix J. S. Bragman, Bojidar Rangelov, et al. "Disease Progression Modeling in Chronic Obstructive Pulmonary Disease." American Journal of Respiratory and Critical Care Medicine 201, no. 3 (2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.

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5

Rooney, William D., Yosef A. Berlow, William T. Triplett, et al. "Modeling disease trajectory in Duchenne muscular dystrophy." Neurology 94, no. 15 (2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.

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ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal ch

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6

Zhou, Jiayu, Jun Liu, Vaibhav A. Narayan, and 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.

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7

Mehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, et al. "Robust parametric modeling of Alzheimer’s disease progression." NeuroImage 225 (January 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.

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8

Sun, Zhaonan, Soumya Ghosh, Ying Li, et al. "A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data." JAMIA Open 2, no. 1 (2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.

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Abstract Objective Chronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management. Materials and Methods We developed a framework to build probabilistic disease progression models using observational medical data. The framework

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9

Gomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin, and Michael Gold. "Modeling Alzheimer's disease progression using the disease system analysis approach." Alzheimer's & Dementia 8, no. 1 (2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.

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10

Cook, Sarah F., and Robert R. Bies. "Disease Progression Modeling: Key Concepts and Recent Developments." Current Pharmacology Reports 2, no. 5 (2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.

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11

Ma, Xiaoke, Long Gao, and Kai Tan. "Modeling disease progression using dynamics of pathway connectivity." Bioinformatics 30, no. 16 (2014): 2343–50. http://dx.doi.org/10.1093/bioinformatics/btu298.

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12

Soper, Braden C., Jose Cadena, Sam Nguyen, et al. "Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors." Journal of the American Medical Informatics Association 29, no. 5 (2022): 864–72. http://dx.doi.org/10.1093/jamia/ocac012.

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Abstract Objective The study sought to investigate the disease state–dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Materials and Methods A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health Syste

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13

Azizi, Tahmineh. "Mathematical Modeling of Cancer Progression." AppliedMath 4, no. 3 (2024): 1065–79. http://dx.doi.org/10.3390/appliedmath4030057.

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Cancer, a complex disease characterized by uncontrolled cell growth and metastasis, remains a formidable challenge to global health. Mathematical modeling has emerged as a critical tool to elucidate the underlying biological mechanisms driving tumor initiation, progression, and treatment responses. By integrating principles from biology, physics, and mathematics, mathematical oncology provides a quantitative framework for understanding tumor growth dynamics, microenvironmental interactions, and the evolution of cancer cells. This study explores the key applications of mathematical modeling in

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14

Yang, Liuqing, Xifeng Wang, Qi Guo, et al. "Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease." Statistics in Biopharmaceutical Research 13, no. 3 (2021): 337–43. http://dx.doi.org/10.1080/19466315.2021.1884129.

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15

Pičulin, Matej, Tim Smole, Bojan Žunkovič, et al. "Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning." JMIR Medical Informatics 10, no. 2 (2022): e30483. http://dx.doi.org/10.2196/30483.

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Background Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Objective Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HC

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16

Noyes, K., A. Bajorska, AR Chappel, et al. "PMC48 “UNNATURAL” HISTORY: MODELING DISEASE PROGRESSION USING OBSERVATIONAL DATA." Value in Health 12, no. 3 (2009): A28. http://dx.doi.org/10.1016/s1098-3015(10)73199-5.

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17

Venkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen, and Stefan Klein. "Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling." NeuroImage 186 (February 2019): 518–32. http://dx.doi.org/10.1016/j.neuroimage.2018.11.024.

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18

Ojha, Vaghawan Prasad, Shantia Yarahmadian, and Madhav Om. "Stochastic Modeling and Simulation of Filament Aggregation in Alzheimer’s Disease." Processes 12, no. 1 (2024): 157. http://dx.doi.org/10.3390/pr12010157.

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Alzheimer’s disease has been a serious problem for humankind, one without a promising cure for a long time now, and researchers around the world have been working to better understand this disease mathematically, biologically and computationally so that a better cure can be developed and finally humanity can get some relief from this disease. In this study, we try to understand the progression of Alzheimer’s disease by modeling the progression of amyloid-beta aggregation, leading to the formation of filaments using the stochastic method. In a noble approach, we treat the progression of filamen

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19

Baum, Larry, and Eric Baum. "Progressive Diseases: Interpretation of Genetic Data." Journal of Theoretical Medicine 2, no. 1 (1999): 1–7. http://dx.doi.org/10.1080/17486709909490784.

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Simple modeling is proposed to represent the screening of gene polymorphisms for association with a progressive disease of insidious onset such as Alzheimer's disease. The modeling demonstrates that when a polymorphism affects the rate of progression as well as the risk of disease, the correct interpretation of DNA data requires an accurate sampling of the living, diseased population. Furthermore, in this population, the effect of the polymorphism on disease risk cannot be distinguished from a corresponding effect on the rate of progression of the disease, and a null result does not preclude a

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20

Das, Satrajit. "Modelling the Progression of Chronic Diseases Using Hidden Markov Models with Electronic Health Records." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48436.

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Progressive diseases like diabetes, chronic kidney disease (CKD), and cardiovascular diseases (CVD) have progressively worsening over time. It is vital to capture and forecast the hidden transitions between disease stages for early intervention. This paper reports a model of disease progression based on Hidden Markov Models (HMMs) learned from longitudinal Electronic Health Records (EHRs). By modeling patient data routinely collected in clinical practice, we predict the probability of unobserved states of disease and future progression trajectories. Our findings show that HMMs can usefully det

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21

Kühnel, Line, Anna‐Karin Berger, Bo Markussen, and Lars L. Raket. "Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales." Statistics in Medicine 40, no. 14 (2021): 3251–66. http://dx.doi.org/10.1002/sim.8932.

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22

Goodison, Steve, Mark E. Sherman, and Yijun Sun. "Computational disease progression modeling can provide insights into cancer evolution." Oncoscience 7, no. 3-4 (2020): 21–22. http://dx.doi.org/10.18632/oncoscience.501.

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23

Kotze, L. "PNS222 IMPUTATION TECHNIQUES FOR MISSING COVARIATES WHEN MODELING DISEASE PROGRESSION." Value in Health 22 (May 2019): S323. http://dx.doi.org/10.1016/j.jval.2019.04.1578.

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24

Karlsson, Kristin E., Justin J. Wilkins, Fredrik Jonsson, Per-Henrik Zingmark, Mats O. Karlsson, and E. Niclas Jonsson. "Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales." AAPS Journal 12, no. 4 (2010): 683–91. http://dx.doi.org/10.1208/s12248-010-9230-0.

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25

Green, C., and S. Zhang. "Modeling Disease Progression In Alzheimer's Dementia To Inform HTA (CEA)." Value in Health 17, no. 7 (2014): A563. http://dx.doi.org/10.1016/j.jval.2014.08.1866.

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26

Liu, Xiaoli, Peng Cao, André R. Gonçalves, Dazhe Zhao, and Arindam Banerjee. "Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso." ACM Transactions on Knowledge Discovery from Data 12, no. 6 (2018): 1–35. http://dx.doi.org/10.1145/3230668.

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27

Donohue, Michael C., Anthony Gamst, Clifford Jack, et al. "F3-02-02: MODELING LONG-TERM DISEASE PROGRESSION WITH COVARIATES." Alzheimer's & Dementia 10 (July 2014): P203—P204. http://dx.doi.org/10.1016/j.jalz.2014.04.253.

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28

Janke, Andrew L., Greig de Zubicaray, Stephen E. Rose, Mark Griffin, Jonathan B. Chalk, and Graham J. Galloway. "4D deformation modeling of cortical disease progression in Alzheimer's dementia." Magnetic Resonance in Medicine 46, no. 4 (2001): 661–66. http://dx.doi.org/10.1002/mrm.1243.

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29

Ozkan, Alican, Gwenn Merry, David B. Chou, et al. "878 MODELING INFLAMMATORY BOWEL DISEASE PROGRESSION IN HUMAN ORGAN-CHIPS." Gastroenterology 164, no. 6 (2023): S—195. http://dx.doi.org/10.1016/s0016-5085(23)01430-0.

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30

Sukkar, Rafid, Bradley Wyman, Elyse Katz, Yanwei Zhang, and David Raunig. "P1-118: Modeling Alzheimer's disease progression using hidden markov models." Alzheimer's & Dementia 7 (July 2011): S147. http://dx.doi.org/10.1016/j.jalz.2011.05.397.

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31

Takahashi, *Koki, Yoshihiro Noda, Daichi Sone, et al. "DISEASE PROGRESSION MODELING OF BRAIN MACROSTRUCTURE IN TREATMENT-RESISTANT DEPRESSION." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i63. https://doi.org/10.1093/ijnp/pyae059.108.

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Abstract Background Progressive abnormalities in brain structures have consistently been reported in depression. However, the distinct trajectories of progressive structural abnormalities in TRD remain unknown. Moreover, treatment-resistant depression (TRD) is suggested to have a heterogeneous pathophysiology. The Subtype and Stage Inference (SuStaIn) algorithm, an unsupervised machine learning technique, has shown premise in distinguishing disease biotypes with different progression trajectories. Aims & Objectives This study aimed to identify patterns of structural abnormalities in TRD wi

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32

Platero, Carlos. "Categorical predictive and disease progression modeling in the early stage of Alzheimer’s disease." Journal of Neuroscience Methods 374 (May 2022): 109581. http://dx.doi.org/10.1016/j.jneumeth.2022.109581.

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33

Nie, Liqiang, Luming Zhang, Lei Meng, Xuemeng Song, Xiaojun Chang, and Xuelong Li. "Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease." IEEE Transactions on Neural Networks and Learning Systems 28, no. 7 (2017): 1508–19. http://dx.doi.org/10.1109/tnnls.2016.2520964.

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34

Singh, Anant Manish. "A Multivariate Joint Modeling Framework for Disease Progression in Chronic Illnesses Using Longitudinal Data." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48268.

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Abstract: Disease progression modeling in chronic illnesses presents significant challenges due to the inherent complexity, heterogeneity and multivariate nature of longitudinal medical data. Traditional approaches often focus on single disease outcomes or fail to capture complex dependencies between multiple biomarkers measured over time. This research introduces a novel multivariate joint modeling framework that integrates advanced Bayesian methods with deep learning techniques to model disease progression trajectories across multiple correlated outcomes. Our framework extends existing metho

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35

Sidhu, Ishnoor, Sonali P. Barwe, Raju K. Pillai, and Anilkumar Gopalakrishnapillai. "Harnessing the Power of Induced Pluripotent Stem Cells and Gene Editing Technology: Therapeutic Implications in Hematological Malignancies." Cells 10, no. 10 (2021): 2698. http://dx.doi.org/10.3390/cells10102698.

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In vitro modeling of hematological malignancies not only provides insights into the influence of genetic aberrations on cellular and molecular mechanisms involved in disease progression but also aids development and evaluation of therapeutic agents. Owing to their self-renewal and differentiation capacity, induced pluripotent stem cells (iPSCs) have emerged as a potential source of short in supply disease-specific human cells of the hematopoietic lineage. Patient-derived iPSCs can recapitulate the disease severity and spectrum of prognosis dictated by the genetic variation among patients and c

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36

Pfeiffer, John, Tim Foley, Eduardo Braun, et al. "Abstract 1917: Accurate modeling of HER2 positive breast cancer disease progression with a biophysical modeling software." Cancer Research 82, no. 12_Supplement (2022): 1917. http://dx.doi.org/10.1158/1538-7445.am2022-1917.

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Abstract Breast cancer (BC) progression during NAT is associated with development of distant metastases, positive LN status, and decreased OS/RFS. These can occur in the context of clinical trials and therapy de-escalation, where the focus is on delivering effective NAT to patients while reducing drug toxicity. The risks added by disease progression underscore the need for early identification of NAT progressors. To this end, we replicated the NeoSphere study in silico using TumorScope (TS), a biophysical modeling software, focusing on predicting disease progression during NAT. The NeoSphere t

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37

Hong, Yun Jeong, Bora Yoon, Yong S. Shim, et al. "Predictors of Clinical Progression of Subjective Memory Impairment in Elderly Subjects: Data from the Clinical Research Centers for Dementia of South Korea (CREDOS)." Dementia and Geriatric Cognitive Disorders 40, no. 3-4 (2015): 158–65. http://dx.doi.org/10.1159/000430807.

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Background/Aims: The aims of this study were to determine baseline factors related to the progression of subjective memory impairment (SMI) in elderly subjects and to develop a new modeling scale to predict progression. Methods: Elderly subjects with SMI were recruited from the nationwide Clinical Research Centers for Dementia of South Korea (CREDOS) multicenter cohort and divided into two groups: (1) progressed to mild cognitive impairment or Alzheimer's disease or (2) stable without progression. Baseline clinical characteristics were compared between the groups, and the most relevant predict

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38

Kim, Darae, Dongwoo Chae, Chi Young Shim, et al. "Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling." Journal of Clinical Medicine 8, no. 9 (2019): 1302. http://dx.doi.org/10.3390/jcm8091302.

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We aimed to develop a mathematical model to predict the progression of aortic stenosis (AS) and aortic dilatation (AD) in bicuspid aortic valve patients. Bicuspid AS patients who underwent at least two serial echocardiograms from 2005 to 2017 were enrolled. Mathematical modeling was undertaken to assess (1) the non-linearity associated with the disease progression and (2) the importance of first visit echocardiogram in predicting the overall prognosis. Models were trained in 126 patients and validated in an additional cohort of 43 patients. AS was best described by a logistic function of time.

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39

Xiaoxia, Li, and Heidari Farzin. "Understanding the Progression of Lewy Body Dementia with Longitudinal Data Modeling." Journal of Management Science and Business Intelligence 5, no. 2 (2020): 1–6. https://doi.org/10.5281/zenodo.3996530.

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Lewy Body Dementia is the second most common dementia type after Alzheimer's disease. The cognitive decline caused by Lewy Body Dementia affects the patients' performance in daily living and the ability to make decisions. It is critical to understand the course of the disease progression and estimate the cognitive decline trajectory for disease management and care planning. This study analyzed the factors that can be used for the Clinical Dementia Rating score estimation and developed a highly customized model using the data obtained from an existing Lewy Body Dementia database. The mo

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40

Cao, Yanguang, Debra C. DuBois, Hao Sun, Richard R. Almon, and William J. Jusko. "Modeling Diabetes Disease Progression and Salsalate Intervention in Goto-Kakizaki Rats." Journal of Pharmacology and Experimental Therapeutics 339, no. 3 (2011): 896–904. http://dx.doi.org/10.1124/jpet.111.185686.

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41

Bhatti, Priha. "Predictive Modeling of Chronic Kidney Disease Progression with Ensemble Learning Techniques." Sukkur IBA Journal of Emerging Technologies 7, no. 2 (2025): 71–82. https://doi.org/10.30537/sjet.v7i2.1449.

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The present study aims to tackle the significant issue of prompt identification of chronic kidney disease (CKD), a highly prevalent and potentially fatal medical illness. Given the crucial function of the kidneys in maintaining homeostasis, we put forth a novel ensemble learning model to forecast the onset of chronic kidney disease (CKD). Utilizing an extensive dataset, the study employs ten carefully designed stages, covering data analysis, missing data management, normalization, and training of machine learning models. The model that we have proposed exhibits superior performance compared to

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42

Maitland, M. L., K. Wu, M. R. Sharma, et al. "Estimation of Renal Cell Carcinoma Treatment Effects From Disease Progression Modeling." Clinical Pharmacology & Therapeutics 93, no. 4 (2012): 345–51. http://dx.doi.org/10.1038/clpt.2012.263.

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43

Asena, Tilahun Ferede, and Ayele Taye Goshu. "Comparison of Sojourn Time Distributions in Modeling HIV/AIDS Disease Progression." Biometrical Letters 54, no. 2 (2017): 155–74. http://dx.doi.org/10.1515/bile-2017-0009.

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Summary An application of semi-Markov models to AIDS disease progression was utilized to find best sojourn time distributions. We obtained data on 370 HIV/AIDS patients who were under follow-up from September 2008 to August 2015, from Yirgalim General Hospital, Ethiopia. The study reveals that within the “good” states, the transition probability of moving from a given state to the next worst state has a parabolic pattern that increases with time until it reaches a maximum and then declines over time. Compared with the case of exponential distribution, the conditional probability of remaining i

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44

Walker, Rachel, Jaime Mejia, Jae K. Lee, et al. "Personalizing Gastric Cancer Screening With Predictive Modeling of Disease Progression Biomarkers." Applied Immunohistochemistry & Molecular Morphology 27, no. 4 (2019): 270–77. http://dx.doi.org/10.1097/pai.0000000000000598.

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45

Jacqmin, Philippe, Ronald Gieschke, Isabelle Delor, et al. "Mathematical Disease Progression Modeling in Type 2/3 Spinal Muscular Atrophy." Muscle & Nerve 58, no. 4 (2018): 528–35. http://dx.doi.org/10.1002/mus.26178.

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46

Sun, Ming, and Yuanjia Wang. "Nonlinear model with random inflection points for modeling neurodegenerative disease progression." Statistics in Medicine 37, no. 30 (2018): 4721–42. http://dx.doi.org/10.1002/sim.7951.

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47

Thomson, J. L., and W. E. Copes. "Modeling Disease Progression of Camellia Twig Blight Using a Recurrent Event Model." Phytopathology® 99, no. 4 (2009): 378–84. http://dx.doi.org/10.1094/phyto-99-4-0378.

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To improve control of camellia twig blight (CTB) using sanitation methods, a more complete epidemiologic understanding of this disease is necessary. Three CTB disease stages were modeled using recurrent event analysis. Wound inoculated stems were observed at regular intervals for appearance of disease symptoms. Survival times (time from inoculation until symptom appearance) for the three disease stages (mild, moderate, and severe) were regressed against stem diameter, monthly mean hours/day within a specified temperature range (15 to 30°C), and season (spring, summer, fall, and winter). For al

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48

Caldwell, Kim A., Corey W. Willicott, and Guy A. Caldwell. "Modeling neurodegeneration in Caenorhabditiselegans." Disease Models & Mechanisms 13, no. 10 (2020): dmm046110. http://dx.doi.org/10.1242/dmm.046110.

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ABSTRACTThe global burden of neurodegenerative diseases underscores the urgent need for innovative strategies to define new drug targets and disease-modifying factors. The nematode Caenorhabditis elegans has served as the experimental subject for multiple transformative discoveries that have redefined our understanding of biology for ∼60 years. More recently, the considerable attributes of C. elegans have been applied to neurodegenerative diseases, including amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease and Huntington's disease. Transgenic nematodes with genes encodin

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49

Ross, Jennifer M., Roger Ying, Connie L. Celum, et al. "Modeling HIV disease progression and transmission at population-level: The potential impact of modifying disease progression in HIV treatment programs." Epidemics 23 (June 2018): 34–41. http://dx.doi.org/10.1016/j.epidem.2017.12.001.

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50

REYES-SILVEYRA, JORGE, ARMIN R. MIKLER, JUSTIN ZHAO, and ANGEL BRAVO-SALGADO. "MODELING INFECTIOUS OUTBREAKS IN NON-HOMOGENEOUS POPULATIONS." Journal of Biological Systems 19, no. 04 (2011): 591–606. http://dx.doi.org/10.1142/s0218339011004007.

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Emerging diseases, novel strains of reemerging diseases, and bioterrorism threats necessitate the development of computational models that can supply health care providers with tools to facilitate analysis and simulation of the progression of infectious diseases in a population. Most computational models assume homogeneous mixing within populations. However, a more realistic approach to the simulation of infectious disease outbreaks includes the stratification of populations in which the interactions between individuals are affinity-based. To examine the effects of heterogeneous populations on

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