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Review Article

Human Health Management and Life/Disease Risk Assessment: A Review

Author(s): Hui-zhong Li, Xiang-dong Liu* and Xin Shi*

Volume 15, Issue 1, 2021

Published on: 18 February, 2020

Page: [30 - 36] Pages: 7

DOI: 10.2174/1872212114666200218112118

Price: $65

Abstract

Background: Modern medical researches show that the health of a population is closely related to their behavior, environmental factors, social and economic conditions, aging and genetic susceptibility. Therefore, it is very important to study the relationship between these factors and health of a population.

Objective: This study reviews the latest research progress in population health management and life/disease risk assessment, analyzes the existing problems in relevant researches, and proposes some improvement plans to solve the problems.

Methods: It also introduces the general methods of human health management and life/disease risk assessment, analyzes the defects in these methods in practical application, and then draws conclusions regarding industrial system equipment health management and residual life prediction, and puts forward some new ideas to solve the problems.

Results: In the study of health assessment, although self-assessment of health is a common method of determining health variables, its subjectivity and inaccuracy are problems that cannot be ignored. In life/disease risk assessment studies, medical effect and efficacy are mainly evaluated, and little consideration is given to other factors, such as behaviour, environment, and socio-economic status. In the construction of statistical graphs, great individual differences, internal symbiotic variables and external impact variables are seldom considered.

Conclusion: This study helps to understand the basic ideas and methods of health management and life/disease risk assessment, and proposes some future research programs based on the author's knowledge.

Keywords: Health management, life risk assessment, disease risk assessment, random filter model, medical statistical charts, the delay time model, residual life prediction.

Graphical Abstract

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