| 1. |
- Bregoli, Alessandro, et al.
(author)
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Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
- 2023
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In: 30th International Symposium on Temporal Representation and Reasoning, TIME 2023. - 9783959772983
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Conference paper (peer-reviewed)abstract
- Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.
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| 2. |
- Liew, Bernard X. W., et al.
(author)
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Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash-associated Disorders Using Bayesian Networks
- 2019
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In: The Clinical Journal of Pain. - : LIPPINCOTT WILLIAMS & WILKINS. - 0749-8047 .- 1536-5409. ; 35:8, s. 647-655
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Journal article (peer-reviewed)abstract
- Objectives: The present study's objective was to understand the causal mechanisms underpinning the recovery of individuals with whiplash-associated disorders (WAD). We applied Bayesian Networks (BN) to answer 2 study aims: (1) to identify the causal mechanism(s) of recovery underpinning neck-specific exercise (NSE), and (2) quantify if the cyclical pathway of the fear-avoidance model (FAM) is supported by the present data.Materials and Methods: We analyzed a prospective cohort data set of 216 individuals with chronic WAD. Fifteen variables were used to build a BN model: treatment group (NSE with or without a behavioral approach, or general physical activity), muscle endurance, range of motion, hand strength, neck proprioception, pain catastrophizing, fear, anxiety, depression, self-efficacy, perceived work ability, disability, pain intensity, sex, and follow-up time.Results: The BN model showed that neck pain reduction rate was greater after NSE compared with physical activity prescription (beta=0.59 points per month [P<0.001]) only in the presence of 2 mediators: global neck muscle endurance and perceived work ability. We also found the following pathway of variables that constituted the FAM: anxiety, followed by depressive symptoms, fear, catastrophizing, self-efficacy, and consequently pain.Conclusions: We uncovered 2 mediators that explained the mechanisms of effect behind NSE, and proposed an alternative FAM pathway. The present study is the first to apply BN modelling to understand the causal mechanisms of recovery in WAD. In doing so, it is anticipated that such analytical methods could increase the precision of treatment of individuals with chronic WAD.
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| 3. |
- Liew, Bernard X. W., et al.
(author)
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Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy
- 2021
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In: European Journal of Pain. - : Wiley. - 1090-3801 .- 1532-2149. ; 25:5, s. 1162-1172
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Journal article (peer-reviewed)abstract
- Background The mechanisms of action that facilitate improved outcomes after conservative rehabilitation are unclear in individuals with cervical radiculopathy (CR). This study aims to determine the pathways of recovery of disability with different exercise programs in individuals with CR. Methods We analysed a dataset of 144 individuals with CR undergoing conservative rehabilitation. Eleven variables collected at baseline, 3, 6 and 12 months follow-up were used to build a Bayesian Network (BN) model: treatment group (neck-specific vs. general exercises), age, sex, self-efficacy, catastrophizing, kinesiophobia, anxiety, neck-arm pain intensity, headache pain intensity and disability. The model was used to quantify the contribution of different mediating pathways on the outcome of disability at 12th months. Results All modelled variables were conditionally independent from treatment groups. A one-point increase in anxiety at 3rd month was associated with a 2.45-point increase in 12th month disability (p <.001). A one-point increase in head pain at 3rd month was associated with a 0.08-point increase in 12th month disability (p <.001). Approximately 83% of the effect of anxiety on disability was attributable to self-efficacy. Approximately 88% of the effect of head pain on disability was attributable to neck-arm pain. Conclusions No psychological or pain-related variables mediated the different treatment programs with respect to the outcome of disability. Thus, the specific characteristics investigated in this study did not explain the differences in mechanisms of effect between neck-specific training and prescribed physical activity. The present study provides candidate modifiable mediators that could be the target of future intervention trials. Significance Psychological and pain characteristics did not differentially explain the mechanism of effect that two exercise regimes had on disability in individuals with cervical radiculopathy. In addition, we found that improvements in self-efficacy was approximately five times more important than that of neck-arm pain intensity in mediating the anxiety-disability relationship. A mechanistic understanding of recovery provides candidate modifiable mediators that could be the target of future intervention trials. Trials registration ClinicalTrials.gov identifier: NCT01547611.
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| 4. |
- Liew, Bernard X W, et al.
(author)
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Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using Bayesian networks
- 2020
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In: European Journal of Pain. - : John Wiley & Sons. - 1090-3801 .- 1532-2149. ; 24:5, s. 909-920
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Journal article (peer-reviewed)abstract
- BACKGROUND: Rehabilitation approaches should be based on an understanding of the mechanisms underpinning functional recovery. Yet, the mediators that drive an improvement in post-surgical pain-related disability in individuals with cervical radiculopathy (CR) are unknown. The aim of the present study is to use Bayesian networks (BN) to learn the probabilistic relationships between physical and psychological factors, and pain-related disability in CR.METHODS: We analysed a prospective cohort dataset of 201 post-surgical individuals with CR. In all, 15 variables were used to build a BN model: age, sex, neck muscle endurance, neck range of motion, neck proprioception, hand grip strength, self-efficacy, catastrophizing, depression, somatic perception, arm pain intensity, neck pain intensity and disability.RESULTS: A one point increase in a change of self-efficacy at 6 months was associated with a 0.09 point decrease in a change in disability at 12 months (t = -64.09, p < .001). Two pathways led to a change in disability: a direct path leading from a change in self-efficacy at 6 months to disability, and an indirect path which was mediated by neck and arm pain intensity changes at 6 and 12 months.CONCLUSIONS: This is the first study to apply BN modelling to understand the mechanisms of recovery in post-surgical individuals with CR. Improvements in pain-related disability was directly and indirectly driven by changes in self-efficacy levels. The present study provides potentially modifiable mediators that could be the target of future intervention trials. BN models could increase the precision of treatment and outcome assessment of individuals with CR.SIGNIFICANCE: Using Bayesian Network modelling, we found that changes in self-efficacy levels at 6-month post-surgery directly and indirectly influenced the change in disability in individuals with CR. A mechanistic understanding of recovery provides potentially modifiable mediators that could be the target of future intervention trials.
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