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AMIA ... Annual Symposium Proceedings.... 2020Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for...
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.
Topics: Chronic Disease; Disease Progression; Humans; Markov Chains; Models, Statistical
PubMed: 33936441
DOI: No ID Found -
Mayo Clinic Proceedings Feb 2018
Topics: Disease Progression; Humans; Kidney Diseases; Kidney Failure, Chronic
PubMed: 29395349
DOI: 10.1016/j.mayocp.2017.12.018 -
Journal of Clinical Periodontology Apr 2020To investigate the role of Epstein-Barr virus (EBV), cytomegalovirus (CMV), and anaerobic bacteria in the progression of periodontitis. (Randomized Controlled Trial)
Randomized Controlled Trial
AIM
To investigate the role of Epstein-Barr virus (EBV), cytomegalovirus (CMV), and anaerobic bacteria in the progression of periodontitis.
METHODS
Eighty-one adults with generalized moderate to severe periodontitis were randomly assigned to: oral hygiene or scaling and root planning ± placebo or polyunsaturated fatty acids fish oil. Subgingival plaque samples collected from three healthy and three disease sites at weeks 0, 16, and 28 and from sites demonstrating disease progression were analysed for EBV, CMV, P. gingivalis (Pg), T. forsythia (Tf), and T. denticola (Td) DNA using quantitative polymerase chain reaction.
RESULTS
Cytomegalovirus was detected in 0.3% (4/1454) sites. EBV was present in 12.2% of healthy sites (89/728) and 27.6% disease sites (201/726; p < .0001), but was in low copy number. Disease progression occurred in 28.4% of participants (23/81) and developed predominantly at sites identified as diseased (75/78; 96.2%). CMV and EBV were not associated with disease progression (p = .13) regardless of treatment. In contrast, disease sites were associated with higher levels of Pg, Td, Tf, and total bacteria, and sites that exhibited disease progression were associated with an abundance of Td and Tf (p < .04).
CONCLUSION
Disease progression was associated with Gram-negative anaerobic bacteria; not EBV or CMV.
Topics: Adult; Cytomegalovirus; Disease Progression; Herpesviridae; Herpesvirus 4, Human; Humans; Periodontitis
PubMed: 31860742
DOI: 10.1111/jcpe.13239 -
American Journal of Nephrology 2018
Topics: Disease Progression; Europe; Humans; Polycystic Kidney Diseases; Polycystic Kidney, Autosomal Dominant
PubMed: 30347387
DOI: 10.1159/000493326 -
Brain : a Journal of Neurology Apr 2021
Topics: Disease Progression; Humans; Parkinson Disease; Probability
PubMed: 33829229
DOI: 10.1093/brain/awab060 -
Current Medicinal Chemistry 2018
Topics: Disease Progression; Humans; Inflammation; Oxidation-Reduction
PubMed: 29697359
DOI: 10.2174/092986732511180417115122 -
Journal of Women's Health (2002) Nov 2023
Topics: Pregnancy; Female; Humans; Thyroid Neoplasms; Disease Progression; Retrospective Studies
PubMed: 37910807
DOI: 10.1089/jwh.2023.0653 -
American Journal of Respiratory and... Jan 2021
Topics: Airway Remodeling; Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive; Respiratory System; Tomography, X-Ray Computed
PubMed: 32910677
DOI: 10.1164/rccm.202008-3158ED -
IEEE Journal of Biomedical and Health... Nov 2021Massively available longitudinal data about long-term disease trajectories of patients provides a golden mine for the understanding of disease progression and efficient...
Massively available longitudinal data about long-term disease trajectories of patients provides a golden mine for the understanding of disease progression and efficient health service delivery. It calls for quantitative modeling of disease progression, which is a tricky problem due to the complexity of the disease progression process as well as the irregularity of time documented in trajectories. In this study, we tackle the problem with the goal of predictively analyzing disease progression. Specifically, we propose a novel Variational Hawkes Process (VHP) model to generalize disease progression and predict future patient states based on the clinical observational data of past disease trajectories. First, Hawkes Process captures the intensity of irregular visits in a trajectory documented to medical facilities and controls the aforementioned information flowing into future visits. Thereafter, the captured intensity is incorporated into a Variational Auto-Encoder to generate the representation of the future partial disease trajectory for a target patient in a predictive manner. To further improve the prediction performance, we equip the proposed model with a disease trajectory discriminator to distinguish the generated trajectories from real ones. We evaluate the proposed model on two public datasets from the MIMIC-III database pertaining to heart failure and sepsis patients, respectively, and one real-world dataset from a Chinese hospital pertaining to heart failure patients with multiple admissions. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines, and may derive a set of practical implications that can benefit a wide spectrum of management and applications on disease progression.
Topics: Databases, Factual; Disease Progression; Heart Failure; Humans
PubMed: 34329176
DOI: 10.1109/JBHI.2021.3101113 -
Cells Jun 2022Epigenetic changes drive early embryonic and later stages of development [...].
Epigenetic changes drive early embryonic and later stages of development [...].
Topics: Cell Differentiation; Disease Progression; Epigenesis, Genetic; Gene Expression Regulation, Developmental; Humans
PubMed: 35741035
DOI: 10.3390/cells11121907