Dataset Information

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

ABSTRACT: Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

SUBMITTER: Gao X

PROVIDER: S-EPMC8143882 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundStructured Professional Judgement (SPJ) is routinely administered in mental health and criminal justice settings but cannot identify violence risk above moderate accuracy. There is no current evidence that violence can be prevented using SPJ. This may be explained by routine application of predictive instead of causal statistical models when standardising SPJ instruments.MethodsWe carried out a prospective cohort study of 409 male and female patients discharged from medium secure services in England and Wales to the community. Measures were taken at baseline (pre-discharge), 6 and 12 months post-discharge using the Historical, Clinical and Risk-20 items version 3 (HCR-20v3) and Structural Assessment of Protective Factors (SAPROF). Information on violence was obtained via the McArthur community violence instrument and the Police National Computer.ResultsIn a lagged model, HCR-20v3 and SAPROF items were poor predictors of violence. Eight items of the HCR-20v3 and 4 SAPROF items did not predict violent behaviour better than chance. In re-analyses considering temporal proximity of risk/ protective factors (exposure) on violence (outcome), risk was elevated due to violent ideation (OR 6.98, 95% CI 13.85-12.65, P<0.001), instability (OR 5.41, 95% CI 3.44-8.50, P<0.001), and poor coping/ stress (OR 8.35, 95% CI 4.21-16.57, P<0.001). All 3 risk factors were explanatory variables which drove the association with violent outcome. Self-control (OR 0.13, 95% CI 0.08-0.24, P<0.001) conveyed protective effects and explained the association of other protective factors with violence.ConclusionsUsing two standardised SPJ instruments, predictive (lagged) methods could not identify risk and protective factors which must be targeted in interventions for discharged patients with severe mental illness. Predictive methods should be abandoned if the aim is to progress from risk assessment to effective risk management and replaced by methods which identify factors causally associated with violence.

Dataset Information

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets