Bayesian Networks for Clinical Decision Support in Lung Cancer Care

Sesen, M. Berkan; Nicholson, Ann E.; Bañares‐Alcántara, René; Kadir, Timor; Brady, Michael

doi:10.1371/journal.pone.0082349

Cited by 124 publications

(92 citation statements)

References 49 publications

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“…Of particular relevance to clinical application, Bayesian inference has long been utilized for classification and prediction (175–177). Bayesian classification has been applied to such quality of care assessment for hospitals (178) prediction of outcomes of medical procedures (158, 178–180), and the association of genetic data with phenotypes (181). …”

Section: Modeling Birth Cohorts Using Networkmentioning

confidence: 99%

Elucidation of Pathways Driving Asthma Pathogenesis: Development of a Systems-Level Analytic Strategy

et al. 2014

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Asthma is a genetically complex, chronic lung disease defined clinically as episodic airflow limitation and breathlessness that is at least partially reversible, either spontaneously or in response to therapy. Whereas asthma was rare in the late 1800s and early 1900s, the marked increase in its incidence and prevalence since the 1960s points to substantial gene × environment interactions occurring over a period of years, but these interactions are very poorly understood (1–6). It is widely believed that the majority of asthma begins during childhood and manifests first as intermittent wheeze. However, wheeze is also very common in infancy and only a subset of wheezy children progress to persistent asthma for reasons that are largely obscure. Here, we review the current literature regarding causal pathways leading to early asthma development and chronicity. Given the complex interactions of many risk factors over time eventually leading to apparently multiple asthma phenotypes, we suggest that deeply phenotyped cohort studies combined with sophisticated network models will be required to derive the next generation of biological and clinical insights in asthma pathogenesis.

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Section: Modeling Birth Cohorts Using Networkmentioning

confidence: 99%

Elucidation of Pathways Driving Asthma Pathogenesis: Development of a Systems-Level Analytic Strategy

et al. 2014

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“…A Bayesian network is a graphical model that represents probabilistic relationships between random variables using a directed acyclic graph (DAG) [17]. The structure of the network can be obtained directly from data, but it is very sensitive to noise, thus requiring large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the relationships of IgG subclasses and complements in membranous lupus nephritis and idiopathic membranous nephropathy

Song

et al. 2017

PLoS ONE

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Membranous lupus nephritis (MLN) and idiopathic membranous nephropathy (IMN) are kidney diseases with similar morphology, but distinct etiologies, both producing glomeruli with immune deposits. Immunoglobulins and complements, the main components of the deposits, can be detected by immunofluorescence (IF) microscopy. Previous researches characterized the immune deposits only individually, but not the interactions between them. To study these relationships we analyzed an IF profile of IgG subclasses and complements (IgG1, IgG2, IgG3, IgG4, C3, C1q, and C4) in 53 and 95 cases of biopsy-confirmed MLNs and IMNs, respectively, mainly using information theory and Bayesian networks. We identified significant entropy differences between MLN and IMN for all markers except C3 and IgG1, but mutual information (a measure of mutual dependence) were not significantly different for all the pairs of markers. The entropy differences between MLN and IMN, therefore, were not attributable to the mutual information. These findings suggest that disease type directly and/or indirectly influences the glomerular deposits of most of IgG subclasses and complements, and that the interactions between any pair of the markers were similar between the two diseases. A Markov chain of IgG subclasses was derived from the mutual information about each pair of IgG subclass. Finally we developed an integrated disease model, consistent with the previous findings, describing the glomerular immune deposits of the IgG subclasses and complements based on a Bayesian network using the Markov chain of IgG subclasses as seed. The relationships between the markers were effectively explored by information theory and Bayesian network. Although deposits of IgG subclasses and complements depended on both disease type and the other markers, the interaction between the markers appears conserved, independent from the disease type. The disease model provided an integrated and intuitive representation of the relationships of the IgG subclasses and complements in MLN and IMN.

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“…Bayesian networks have been used extensively in biomedical applications to aid in understanding of disease prognosis and clinical prediction [30, 31] and guide the selection of the appropriate treatment [32, 33] in clinical decision support systems. Lucas et al [17] provide a comprehensive review of Bayesian networks in medical applications.…”

Section: Applying Ipcw With Existing Machine Learning Techniques: mentioning

confidence: 99%

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting

Vock

Wolfson

Bandyopadhyay

et al. 2016

Journal of Biomedical Informatics

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Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5 years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include 1) discarding those observations, 2) treating them as non-events, 3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.

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Bayesian Networks for Clinical Decision Support in Lung Cancer Care

Cited by 124 publications

References 49 publications

Elucidation of Pathways Driving Asthma Pathogenesis: Development of a Systems-Level Analytic Strategy

Elucidation of Pathways Driving Asthma Pathogenesis: Development of a Systems-Level Analytic Strategy

Dissecting the relationships of IgG subclasses and complements in membranous lupus nephritis and idiopathic membranous nephropathy

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting

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