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

2022-06-01
Oflaz, Zarina
Yozgatlıgil, Ceylan
Kestel, Sevtap Ayşe
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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

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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.
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