Bayesian networks in healthcare: What is preventing their adoption?

Kyrimi, Evangelia; Dube, Kudakwashe; Fenton, Norman; Fahmi, Ali; Neves, Mariana R.; Marsh, William; McLachlan, Scott

doi:10.1016/j.artmed.2021.102079

Cited by 27 publications

(21 citation statements)

References 85 publications

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“…Even if causal models have good performance and produce explainable outputs, they will not be useful tools unless their outputs can be presented in a form that end-users can understand, interpret and use. The barriers to implementation and uptake that decision support tools including BNs face are widely reported [42, 43]…”

Section: Discussionmentioning

confidence: 99%

Predicting the causative pathogen among children with pneumonia using a causal Bayesian network

Mascaro

Bhuiyan

et al. 2022

Preprint

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BackgroundPneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incoporating both domain expert knowledge and numerical data.MethodsWe used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative pathogens for childhood pneumonia. Expert knowledge elicitation occurred through a series of group workshops, surveys and one-on-one meetings involving 6-8 experts from diverse domain areas. The model performance was evaluated based on both quantitative metrics (area under the receiver-operator curve (AUROC) and log loss) and qualitative expert validation. Sensitivity analyses were conducted to investigate how the target output is influenced by varying key assumptions of particular high degree of uncertainty around data or domain expert knowledge.ResultsDesigned to apply to a cohort of children with X-ray confirmed pneumonia who presented to a tertiary paediatric hospital in Australia, the resulting BN offers explainable and quantitative predictions on a range of variables of interest, including the diagnosis of bacterial pneumonia, detection of respiratory pathogens in the nasopharynx, and the clinical phenotype of a pneumonia episode. Satisfactory numeric performance has been achieved including an AUROC of 0.8 in predicting the clinical diagnosis of bacterial pneumonia. Three commonly encountered scenarios were presented to demonstrate the potential usefulness of the BN outputs in various clinical pictures.ConclusionsTo our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. It can be utilized to derive recommendations to support more directed and judicious use of antimicrobials for relevant cohorts. The BN needs further validation before it can be clinically implemented. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.

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Section: Discussionmentioning

confidence: 99%

Predicting the causative pathogen among children with pneumonia using a causal Bayesian network

Mascaro

Bhuiyan

et al. 2022

Preprint

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“…Despite the increased amount of investment (of expertise thus effort and time) required to create the models, working with medical experts to co-develop BNs compensates for data limitations that can be both systematic and significant. This improves model predictions and helps to illuminate the clinical problem at hand, increasing the likelihood that any decision support tools arising from these models will be understood, accepted and hence used in clinical care [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data

Ramsay

Mascaro

Campbell

et al. 2022

BMC Med Res Methodol

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Background Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination. Clinicians need to weigh a range of observations to make timely diagnostic and management decisions, a difficult task to achieve without support due to the complex interactions among relevant factors. Directed acyclic graphs (DAG) and causal Bayesian networks (BN) offer a way to explicitly outline the underlying disease, contamination and diagnostic processes, and to further make quantitative inference on the event of interest thus serving as a tool for decision support. Methods We prospectively collected data on children present to ED with suspected UTIs. Through knowledge elicitation workshops and one-on-one meetings, a DAG was co-developed with clinical domain experts (the Expert DAG) to describe the causal relationships among variables relevant to paediatric UTIs. The Expert DAG was combined with prospective data and further domain knowledge to inform the development of an application-oriented BN (the Applied BN), designed to support the diagnosis of UTI. We assessed the performance of the Applied BN using quantitative and qualitative methods. Results We summarised patient background, clinical and laboratory characteristics of 431 episodes of suspected UTIs enrolled from May 2019 to November 2020. The Expert DAG was presented with a narrative description, elucidating how infection, specimen contamination and management pathways causally interact to form the complex picture of paediatric UTIs. Parameterised using prospective data and expert-elicited parameters, the Applied BN achieved an excellent and stable performance in predicting Escherichia coli culture results, with a mean area under the receiver operating characteristic curve of 0.86 and a mean log loss of 0.48 based on 10-fold cross-validation. The BN predictions were reviewed via a validation workshop, and we illustrate how they can be presented for decision support using three hypothetical clinical scenarios. Conclusion Causal BNs created from both expert knowledge and data can integrate case-specific information to provide individual decision support during the diagnosis of paediatric UTIs in ED. The model aids the interpretation of culture results and the diagnosis of UTIs, promising the prospect of improved patient care and judicious use of antibiotics.

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“…In addition, in case of dichotomized variables, a BN can provide the conditional probability that a variable is present or absent, based on the presence or absence of other variables in the network. BNs have been widely used in medicine to predict outcomes such as diagnosis, functional outcome, quality of life and survival, based on patient and disease characteristics 10 – 15 . The advantage compared to other probabilistic modelling methods is that they do not need dedicated input and output variables and that they can be constructed in case of insufficient available evidence on associations between variables 15 .…”

Section: Introductionmentioning

confidence: 99%

“…BNs have been widely used in medicine to predict outcomes such as diagnosis, functional outcome, quality of life and survival, based on patient and disease characteristics 10 – 15 . The advantage compared to other probabilistic modelling methods is that they do not need dedicated input and output variables and that they can be constructed in case of insufficient available evidence on associations between variables 15 . BNs are also easy to understand: The graphical structure makes associations between variables directly interpretable and the provided conditional probabilities align with clinical reasoning 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer

Stap

Haaften

Marrewijk

et al. 2022

Sci Rep

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Although patients with advanced cancer often experience multiple symptoms simultaneously, clinicians usually focus on symptoms that are volunteered by patients during regular history-taking. We aimed to evaluate the feasibility of a Bayesian network (BN) model to predict the presence of simultaneous symptoms, based on the presence of other symptoms. Our goal is to help clinicians prioritize which symptoms to assess. Patient-reported severity of 11 symptoms (scale 0–10) was measured using an adapted Edmonton Symptom Assessment Scale (ESAS) in a national cross-sectional survey among advanced cancer patients. Scores were dichotomized (< 4 and ≥ 4). Using fourfold cross validation, the prediction error of 9 BN algorithms was estimated (Akaike information criterion (AIC). The model with the highest AIC was evaluated. Model predictive performance was assessed per symptom; an area under curve (AUC) of ≥ 0.65 was considered satisfactory. Model calibration compared predicted and observed probabilities; > 10% difference was considered inaccurate. Symptom scores of 532 patients were collected. A symptom score ≥ 4 was most prevalent for fatigue (64.7%). AUCs varied between 0.60 and 0.78, with satisfactory AUCs for 8/11 symptoms. Calibration was accurate for 101/110 predicted conditional probabilities. Whether a patient experienced fatigue was directly associated with experiencing 7 other symptoms. For example, in the absence or presence of fatigue, the model predicted a 8.6% and 33.1% probability of experiencing anxiety, respectively. It is feasible to use BN development for prioritizing symptom assessment. Fatigue seems most eligble to serve as a starting symptom for predicting the probability of experiencing simultaneous symptoms.

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Bayesian networks in healthcare: What is preventing their adoption?

Cited by 27 publications

References 85 publications

Predicting the causative pathogen among children with pneumonia using a causal Bayesian network

Predicting the causative pathogen among children with pneumonia using a causal Bayesian network

Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data

The feasibility of a Bayesian network model to assess the probability of simultaneous symptoms in patients with advanced cancer

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