Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models.Bayesian networks; hierarchical model; diarrhea infection; disease determinants; logistic regression

Подходы к диагностике согласованности данных в байесовских сетях доверия

Bayesian belief networks provide the ability to combine different types of information, e.g. statistical or expert data, allow working with incomplete or inaccurate information; they have clarity and other useful properties. Due to this, Bayesian belief networks have become a popular and highly effective tool in many fields of research. However, in many research areas data provided by the experts can be incoherent, and so in some tasks one should use tools to verify their coherence. The paper discusses examples of application of the Bayesian belief networks in medicine and public health, ecology, economics and risk analysis, functional safety, sociology, and other research areas, and shows the need to develop methods to check the coherence of initial data. The purpose of this work is to systematize problems and examples that illustrate the use of Bayesian belief networks by reviewing and to assess their use of data coherence diagnosis and its importance.Байесовские сети доверия предоставляют возможность объединения нескольких видов информации, например полученной от экспертов или статистически, позволяют работать с неполной или неточной информацией, обладают наглядностью и другими полезными свойствами. Благодаря этому они стали популярным и весьма эффективным инструментом. Однако во многих областях исследования исходные используются полученные от экспертов данные, которые могут быть не согласованы, и поэтому в некоторых задачах следует использовать инструменты для проверки их согласованности. В работе рассмотрены примеры применения аппарата байесовских сетей доверия в медицине и здравоохранении, экологии, экономике и риск-анализе, функциональной безопасности, социологии и других предметных областях и показана необходимость разработки методов для проверки согласованности исходных данных. Цель работы – систематизировать с помощью обзора примеры и задачи, в которых применяются байесовские сети доверия, чтобы оценить, в какой степени в этих задачах учитывается диагностика согласованности исходных данных, и насколько важным является ее применение

Improving Construction Project Schedules before Execution

The construction industry has been forever blighted by delay and disruption. To address this problem, this study proposes the Fitzsimmons Method (FM method) to improve the scheduling performance of activities on the Critical Path before the project execution. The proposed FM method integrates Bayesian Networks to estimate the conditional probability of activity delay given its predecessor and Support Vector Machines to estimate the time delay. The FM method was trained on 302 completed infrastructure construction projects and validated on a £40 million completed road construction project. Compared with traditional Monte Carlo Simulation results, the proposed FM method is 52% more accurate in predicting the projects’ time delay. The proposed FM method contributes to leveraging the vast quantities of data available to improve the estimation of time risk on infrastructure and construction projects

요역동학적 방광출구폐색의 비침습적 예측인자: 베이지안 네트워크 모델을 활용한 분석

학위논문 (석사)-- 서울대학교 대학원 : 의학과(비뇨기과학전공), 2014. 2. 오승준.Purpose: Numerous attempts have been made to predict urodynamic bladder outlet obstruction (BOO), however, little information exists on non-invasive parameters for BOO prediction. We aimed to identify non-invasive clinical parameters to predict BOO using causal Bayesian networks (CBN). Methods: From October 2004 to December 2011, patients with lower urinary tract symptoms (LUTS) suggestive of BPH were included in this study. Out of the 1352 patients, 866 were selected for the analysis. Mean age, total prostate volume (TPV) and IPSS were 66.3 (±7.0, SD) years, 49.8 (±26.7) ml, and 18.0 (±7.7), respectively. Mean bladder outlet obstruction index (BOOI) was 34.0 (± 24.4), and 292 (33.5%) patients had urodynamic BOO (BOOI ≥40). Non-invasive predictors of BOO were selected using CBN. BOO prediction with selected parameters was verified using logistic regression (LR) and artificial neural networks (ANN) considering whole non-invasive parameters. Results: CBN identified TPV, Qmax, PVR, and IPSS item 5 (slow-stream) as independent predictors of BOO. With these four parameters, sensitivity and specificity of BOO prediction were 54.1% and 86.4%, respectively, with an area under receiver operating characteristic curve (AUROC) of 0.793. LR and ANN models with the same parameters showed similar accuracy (LR: sensitivity 51.7%, specificity 90.9%, AUROC 0.797ANN: sensitivity 43.7%, specificity 92.7%, AUROC 0.756). The AUROC of ANN was smaller than that of the other two methods (p-value range <0.001-0.005). Conclusions: Our study demonstrated that TPV, Qmax, PVR, and IPSS item 5 (slow-stream) are independent predictors of urodynamic BOO.Introduction 2 Abstract i Contents iii List of tables and figures iv List of abbreviations v Material and Methods 4 Results 9 Discussion 11 Conclusions 18 Acknowledgements 19 References 20 Figures 26 Tables 30 Abstract in Korean 32Maste

Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders

Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models

A proof-of-concept study to construct Bayesian network decision models for supporting the categorization of sudden unexpected infant death

Sudden infant death syndrome (SIDS) remains a leading cause of infant death in high-income countries. Supporting models for categorization of sudden unexpected infant death into SIDS/non-SIDS could reduce mortality. Therefore, we aimed to develop such a tool utilizing forensic data, but the reduced number of SIDS cases renders this task inherently difficult. To overcome this, we constructed Bayesian network models according to diagnoses performed by expert pathologists and created conditional probability tables in a proof-of-concept study. In the diagnostic support model, the data of 64 sudden unexpected infant death cases was employed as the training dataset, and 16 known-risk factors, including age at death and co-sleeping, were added. In the validation study, which included 8 new cases, the models reproduced experts' diagnoses in 4 or 5 of the 6 SIDS cases. Next, to confirm the effectiveness of this approach for onset prediction, the data from 41 SIDS cases was employed. The model predicted that the risk of SIDS in 0- to 2-month-old infants exposed to passive smoking and co-sleeping is eightfold higher than that in the general infant population, which is comparable with previously published findings. The Bayesian approach could be a promising tool for constructing SIDS prevention models