A rule-based framework for risk assessment in the health domain

Risk Prediction Models for Depression in Community-Dwelling Older Adults

OBJECTIVE

To develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression.

DESIGN

Prospective study.

SETTING

The Survey of Health, Ageing and Retirement in Europe (SHARE) study.

PARTICIPANTS

Participants were community residing adults aged 55 years or older.

MEASUREMENTS

The outcome was presence of depression at a 2-year follow up evaluation. Risk factors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator.

RESULTS

The authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar performance (AUC: 0.730-0.743). In the combined depressed and non-depressed participants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80-0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79-0.82) had satisfactory accuracy.

CONCLUSIONS

The Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calculator based on the streamlined Manto model is freely available at https://manto.unife.it/ for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels.

Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network

INTRODUCTION

Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research.

METHOD

As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was established using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures.

RESULTS

Taking chemistry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from different agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents.

CONCLUSIONS

Results from the chemistry laboratories demonstrate the credibility of the model.

PRACTICAL APPLICATIONS

This study may help provide technical support for risk management in university laboratories.