Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions

Survivability modelling using Bayesian network for patients with first and secondary primary cancers

BACKGROUND AND OBJECTIVE

Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables.

METHODS

In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches.

RESULTS

The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.

CONCLUSIONS

Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring.

Medical idioms for clinical Bayesian network development

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.

Predicting the causative pathogen among children with osteomyelitis using Bayesian networks - improving antibiotic selection in clinical practice

Infection of bone, osteomyelitis (OM), is a serious bacterial infection in children requiring urgent antibiotic therapy. While biological specimens are often obtained and cultured to guide antibiotic selection, culture results may take several days, are often falsely negative, and may be falsely positive because of contamination by non-causative bacteria. This poses a dilemma for clinicians when choosing the most suitable antibiotic. Selecting an antibiotic which is too narrow in spectrum risks treatment failure; selecting an antibiotic which is too broad risks toxicity and promotes antibiotic resistance. We have developed a Bayesian Network (BN) model that can be used to guide individually targeted antibiotic therapy at point-of-care, by predicting the most likely causative pathogen in children with OM and the antibiotic with optimal expected utility. The BN explicitly models the complex relationship between the unobserved infecting pathogen, observed culture results, and clinical and demographic variables, and integrates data with critical expert knowledge under a causal inference framework. Development of this tool resulted from a multidisciplinary approach, involving experts in infectious diseases, modelling, paediatrics, microbiology, computer science and statistics. The model-predicted prevalence of causative pathogens among children with osteomyelitis were 56 % for Staphylococcus aureus, 17 % for 'other' culturable bacteria (like Streptococcus pyogenes), and 27 % for bacterial pathogens that are not culturable using routine methods (like Kingella kingae). Log loss cross-validation suggests that the model performance is robust, with the best fit to culture results achieved when data and expert knowledge were combined during parameterisation. AUC values of 0.68 - 0.77 were achieved for predicting culture results of different types of specimens. BN-recommended antibiotics were rated optimal or adequate by experts in 82-98% of 81 cases sampled from the cohort. We have demonstrated the potential use of BNs in improving antibiotic selection for children with OM, which we believe to be generalisable in the development of a broader range of decision support tools. With appropriate validation, such tools might be effectively deployed for real-time clinical decision support, to promote a shift in clinical practice from generic to individually-targeted antibiotic therapy, and ultimately improve the management and outcomes for a range of serious bacterial infections.

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks

Objectives

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach.

Setting

EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences.

Results

Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density.

Conclusion

Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

Modeling and predicting the occurrence of brain metastasis from lung cancer by Bayesian network: a case study of Taiwan

The Bayesian network (BN) is a promising method for modeling cancer metastasis under uncertainty. BN is graphically represented using bioinformatics variables and can be used to support an informative medical decision/observation by using probabilistic reasoning. In this study, we propose such a BN to describe and predict the occurrence of brain metastasis from lung cancer. A nationwide database containing more than 50,000 cases of cancer patients from 1996 to 2010 in Taiwan was used in this study. The BN topology for studying brain metastasis from lung cancer was rigorously examined by domain experts/doctors. We used three statistical measures, namely, the accuracy, sensitivity, and specificity, to evaluate the performances of the proposed BN model and to compare it with three competitive approaches, namely, naive Bayes (NB), logistic regression (LR) and support vector machine (SVM). Experimental results show that no significant differences are observed in accuracy or specificity among the four models, while the proposed BN outperforms the others in terms of sampled average sensitivity. Moreover the proposed BN has advantages compared with the other approaches in interpreting how brain metastasis develops from lung cancer. It is shown to be easily understood by physicians, to be efficient in modeling non-linear situations, capable of solving stochastic medical problems, and handling situations wherein information are missing in the context of the occurrence of brain metastasis from lung cancer.