Estimation of disease progression for ischemic heart disease using latent Markov with covariates

2022-06-01
Oflaz, Zarina
Yozgatlıgil, Ceylan
Kestel, Sevtap Ayşe
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
216
views
0
downloads
Contemporaneous monitoring of disease progression, in addition to early diagnosis, is important for the treatment of patients with chronic conditions. Chronic disease-related factors are not easily tractable, and the existing data sets do not clearly reflect them, making diagnosis difficult. The primary issue is that databases maintained by health care, insurance, or governmental organizations typically do not contain clinical information and instead focus on patient appointments and demographic profiles. Due to the lack of thorough information on potential risk factors for a single patient, investigations on the nature of disease are imprecise. We suggest the use of a latent Markov model with variables in a latent process because it enables the panel analysis of many forms of data. The purpose of this study is to evaluate unobserved factors in ischemic heart disease (IHD) using longitudinal data from electronic health records. Based on the results we designate states as healthy, light, moderate, and severe to represent stages of disease progression. This study demonstrates that gender, patient age, and hospital visit frequency are all significant factors in the development of the disease. Females acquire IHD more rapidly than males, frequently developing from moderate and severe disease. In addition, it demonstrates that individuals under the age of 20 bypass the light state of IHD and proceed directly to the moderate state.
age, disease progression, electronic health records, gender, ischemic heart disease, latent Markov, longitudinal data, MODEL, RISK, MORTALITY, WOMEN
https://hdl.handle.net/11511/98880
STATISTICAL ANALYSIS AND DATA MINING
Department of Statistics, Article

Suggestions

Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
Polat, Gorkem; Kani, Haluk Tarik; Ergenc, Ilkay; Alahdab, Yesim Ozen; Temizel, Alptekin; Atug, Ozlen (2022-11-01)
Background Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. Methods The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep ...
Modeling comorbidity of chronic diseases using coupled hidden Markov model with bivariate discrete copula
Oflaz, Zarina; Yozgatlıgil, Ceylan; Kestel, Sevtap Ayşe (2023-1-01)
A range of chronic diseases have a significant influence on each other and share common risk factors. Comorbidity, which shows the existence of two or more diseases interacting or triggering each other, is an important measure for actuarial valuations. The main proposal of the study is to model parallel interacting processes describing two or more chronic diseases by a combination of hidden Markov theory and copula function. This study introduces a coupled hidden Markov model with the bivariate discrete cop...
The serum angiotensin converting enzyme and lysozyme levels in patients with ocular involvement of autoimmune and infectious diseases
Sahin, Ozlem; Ziaei, Alireza; Karaismailoğlu, Eda; Taheri, Nusret (Springer Science and Business Media LLC, 2016-2-16)
Background: Increased serum levels of angiotensin converting enzyme and lysozyme are considered as inflammatory markers for diagnosis of sarcoidosis which is an autoimmune inflammatory disease. The purpose of this study is to evaluate the significance of differences in serum angiotensin converting enzyme and lysozyme levels of patients with ocular involvement of other autoimmune inflammatory and infectious diseases. Methods: This is a prospective study involving patients with ankylosing spondylitis, behc...
Capture of rare circulating tumor cells from blood on bio-activated oxide surface inside microfluidic channels
Ateş, Hatice Ceren; Külah, Haluk; Özgür, Ebru; Department of Micro and Nanotechnology (2018)
Isolation and characterization of circulating tumor cells (CTCs) have important clinical significance in terms of prognosis and early detection of response to treatment. Moreover, downstream characterization of CTCs may help better patient stratification and therapy guidance. However, CTCs are extremely rare (~10 CTCs/1010 peripheral blood cells) and highly sensitive, and specific technology is required for their isolation. Rapidly developing microfluidic technologies offer variety of advantages in rare cel...
Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference
Akkoyun, Emrah; Kwon, Sebastian T.; Acar, Aybar Can; Lee, Whal; Baek, Seungik (Elsevier BV, 2020-02-01)
Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically importan...
Citation Formats
Z. Oflaz, C. Yozgatlıgil, and S. A. Kestel, “Estimation of disease progression for ischemic heart disease using latent Markov with covariates,” STATISTICAL ANALYSIS AND DATA MINING, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/98880.
METU IIS - Integrated Information Service open@metu.edu.tr