A Reliability Model for the Progression of Chronic Heart Failure

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.

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

Oflaz

Yozgatlıgil

Selcuk‐Kestel

2022

Statistical Analysis

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The impact of Markov model to predict the status of children with congenital heart disease at post-operative ICU

Sharwardy

Sarwar

Rahman

2023

Int. j. inf. tecnol.

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An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Shetaban

Esfahani

Saghaei

et al. 2020

Computational Intelligence

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Today, air pollution, smoking, use of fatty acids and ready-made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro-simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity

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A Reliability Model for the Progression of Chronic Heart Failure

Cited by 3 publications

References 3 publications

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

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

The impact of Markov model to predict the status of children with congenital heart disease at post-operative ICU

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

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