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Role of Knowledge Graphs in Analyzing Epidemics and Health Disasters
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
Multi-morbidity is defined as the coexistence of more than one disease (chronic or acute) in a single human being. When more than one disease exists together in same human being then they are called comorbid diseases. Comorbidity increases the chances of failure of the treatment for single diseases and hence may lead to severe conditions of the patient. Generally, the knowledge graphs created for this purpose are: Knowledge graph of genes and phenotypes,Knowledge graph of diseases,Knowledge graph of cures and medicines.
Multi-Level Data Analysis in Cancer
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
Comorbidity is defined as the presence of one or more secondary diseases associated to a primary disease. The incidence of such comorbidities increases with age, having a high impact on life expectancy, which decreases considerably in the presence of a handful of simultaneous diseases,1 as observed in ageing populations.2 Complementarily to direct comorbidity relations, there are also diseases that protect against the development of specific secondary diseases, also known as inverse comorbidity. One of the most renowned examples of inverse comorbidity relations is the one described between central nervous system disorders and cancer.3
Polynomial Exploratory Factor Analysis on Molecular Dynamics Trajectory of the Ras-GAP System: A Possible Theoretical Approach to Enzyme Engineering
Published in Evgeni Starikov, Bengt Nordén, Shigenori Tanaka, Entropy-Enthalpy Compensation, 2020
E. B. Starikov, Kohei Shimamura, Shota Matsunaga, Shigenori Tanaka
The term "comorbidity” comes from experimental/statistical medicine and means the presence of one or more additional conditions co-occurring with (i,e., concomitant or concurrent with) the primary condition. In the "tetrachoric” case, we study some further changing parameter—taking the binary values [say, Yes/No)—and introducing thus some additional dichotomy into the primary case. In the first line, this should be connected with the planning of experiments.
Association between phthalates and sleep problems in the U.S. adult females from NHANES 2011-2014
Published in International Journal of Environmental Health Research, 2023
Xiaomei Wu, Shan Liu, Lin Wen, Yuxuan Tan, Huixian Zeng, Huanzhu Liang, Xueqiong Weng, Yingying Wu, Huojie Yao, Yingyin Fu, Zhiyu Yang, Yexin Li, Qian Chen, Zurui Zeng, Qiaoyuan Fei, Ruihua Wang, Chunxia Jing
Potential covariates identified based on previous studies related to the effects of environmental chemical exposure on sleep. We determined the minimum sufficient set of potential covariables (Rahman et al. 2022; Zamora et al. 2023), including age group (20–39 years, 40–59 years, and≥60 years), race (Mexican American, other Hispanic, Non-Hispanic White, Non-Hispanic Black, other Race – Including Multi-Racial), education (Less Than 9th Grade, 9-11th grade (Includes 12th grade with no diploma), High school graduate/General educational development (GED) or equivalent, some college or Associate of Arts degree (AA), College Graduate or above), exercise (according to the amount of recreational activity in a typical day classified as “Mild” or “Moderate and above”), drinking (Yes, No; ≥ 12 or < 12 alcoholic drinks per year), poverty income ratio (PIR) (<5, ≥5), marital status (Married, Solitude, Unmarried, Cohabitation), BMI (Underweight, Normal, Overweight, Obesity), Urinary creatinine, comorbidity index (0, 1, 2, 3, or≥4; Have been told they have chronic diseases: hypertension, diabetes, cancer, nephropathy, arthritis), serum cotinine (biomarker of smoking status), and depression (Yes, No). Poverty income ratio (PIR) referred to family poverty income ratio, which was calculated by dividing family income by the poverty guidelines, specified to appropriate year and participant’s state. In cases where family income was reported as a range value, the midpoint of the range was used to compute PIR. Low PIR represents a higher degree of poverty (Okosun et al. 2014) (<5, ≥5) (Shiue 2017). Solitude means widowed/divorced/separated, unmarried means never married, and cohabitation means living with partner. Body mass index (BMI) was calculated by dividing the weight of the participant in kilograms by the height squared (meters) of the individual. Urinary creatinine was used to adjust for urinary dilution, including urinary creatinine as a covariate in the models was done to minimize bias (Barr et al. 2005undefined. Comorbidity index was important for longitudinal research because it can reflect a person’s health status through combined diseases (Charlson et al. 2008; Kravitz et al. 2021). Hypertension, diabetes, cancer, nephropathy, and arthritis represented comorbidities and were included as covariates in our study. The number of subjects reporting conditions was then combined to generate an ordinal comorbidity index (Fantus et al. 2018). Serum cotinine was used as a marker for tobacco exposure. The Patient Health Questionnaire (PHQ) was a self-administered version of the PRIME-MD (Primary Care Evaluation of Mental Disorders) diagnostic instrument for common mental disorders. PHQ-9 was used to assess the presence and severity of depression in participants in the past two weeks (Spitzer et al. 1999; Kroenke et al. 2001). The PHQ-9 was the depression module, which scores each of the 9 DSM-IV criteria on a scale from “0” (not at all) to “3” (nearly every day) (Kroenke et al. 2001. Age, BMI (kg/m2), urinary creatinine, and serum cotinine at follow-up were continuous variables.