Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

A methodology for sensor modeling and placement optimization to support temperature monitoring

This paper presents a modeling and optimization approach for sensor placement in a building zone that supports reliable environment monitoring. © 2012 ACM.

How Mothers Talk About Placement of Their Child With Autism Outside the Home

In this article I use insights offered by the poststructural shift and linguistic turn in social scientific inquiry, specifically discourse analysis, to explore mothers’ talk about the placement of their child with autism outside of the home. By viewing mothers’ talk as data, I bring to light the discourses and interpretive practices that mothers drew on to organize their talk of placement. In doing so, I provide insights into how mothers gave meaning to processes of placement while also expanding on commonsensical discursive notions of “good” mothering, caregiving, and family. Implications of the findings are discussed.

Can less be more? Comparison of an 8-item placement quality measure with the 50-item Dundee Ready Educational Environment Measure (DREEM)

Clinical clerks learn more than they are taught and not all they learn can be measured. As a result, curriculum leaders evaluate clinical educational environments. The quantitative Dundee Ready Environment Measure (DREEM) is a de facto standard for that purpose. Its 50 items and 5 subscales were developed by consensus. Reasoning that an instrument would perform best if it were underpinned by a clearly conceptualized link between environment and learning as well as psychometric evidence, we developed the mixed methods Manchester Clinical Placement Index (MCPI), eliminated redundant items, and published validity evidence for its 8 item and 2 subscale structure. Here, we set out to compare MCPI with DREEM. 104 students on full-time clinical placements completed both measures three times during a single academic year. There was good agreement and at least as good discrimination between placements with the smaller MCPI. Total MCPI scores and the mean score of its 5-item learning environment subscale allowed ten raters to distinguish between the quality of educational environments. Twenty raters were needed for the 3-item MCPI training subscale and the DREEM scale and its subscales. MCPI compares favourably with DREEM in that one-sixth the number of items perform at least as well psychometrically, it provides formative free text data, and it is founded on the widely shared assumption that communities of practice make good learning environments.

Comparison of Composite Placement Techniques by Recently Qualified Dental Practitioners.

Objectives: To evaluate the placement of composite materials by new graduates using three alternative placement techniques.Methods: A cohort of 34 recently qualified graduates were asked to restore class II interproximal cavities in plastic teeth using three different techniques.

(i) A conventional incremental filling technique (Herculite XRV) using increments no larger than 2-mm with an initial layer on the cervical floor of the box of 1-mm.
(ii) Flowable bulk fill technique (Dentsply SDR) bulk fill placement in a 3-mm layer followed by an incremental fill of a microhybrid resin
(iii) Bulk fill (Kerr Sonicfill) which involved restorations placed in a 5-mm layer.

The operators were instructed in each technique, didactically and with a hands-on demonstration, prior to restoration placement.
All restorations were cured according to manufacturer’s recommendations. Each participant restored 3 teeth, 1 tooth per treatment technique.
The restorations were evaluated using modified USPHS criteria to assess both the marginal adaptation and the surface texture of the restorations. Blind evaluations were carried out independently by two examiners with the aid of magnification (loupes X2.5). Examiners were standardized prior to evaluation.
Results: Gaps between the tooth margins and the restoration or between the layers of the restoration were found in 13 of Group (i), 3 of Group (ii), and 4 of Group (iii)
Statistical analysis revealed a significant difference between the incrementally filled group (i) and the flowable bulk-fill group (ii) (p=0.0043) and between the incrementally filled (i) and the bulk fill groups (iii) (p=0.012) and no statistical difference (p=0.69) between the bulk filled groups Conclusions: Bulk fill techniques may result in a more satisfactory seal of the cavity margins when restoring with composite.

Improving composite damage modelling through automatic placement of cohesive elements

Numerous experimental studies of damage in composite laminates have shown that intralaminar (in-plane) matrix cracks lead to interlaminar delamination (out-of-plane) at ply interfaces. The smearing of in-plane cracks over a volume, as a consequence of the use of continuum damage mechanics, does not always effectively capture the full extent of the interaction between the two failure mechanisms. A more accurate representation is obtained by adopting a discrete crack approach via the use of cohesive elements, for both in-plane and out-of-plane damage. The difficulty with cohesive elements is that their location must be determined a priori in order to generate the model; while ideally the position of the crack migration, and more generally the propagation path, should be obtained as part of the problem’s solution. With the aim of enhancing current modelling capabilities with truly predictive capabilities, a concept of automatic insertion of interface elements is utilized. The consideration of a simple traction criterion in relation to material strength, evaluated at each node of the model (or of the regions of the model where it is estimated cracks might form), allows for the determination of initial crack location and subsequent propagation by the insertion of cohesive elements during the course of the analysis. Several experimental results are modelled using the commercial package ABAQUS/Standard with an automatic insertion subroutine developed in this work, and the results are presented to demonstrate the capabilities of this technique.

The inside story: A survey of social work students’ supervision and learning opportunities on placement

Practice learning accounts for half of the content of the Bachelor of Social Work degree course requirements in Northern Ireland in their field education programs and share a professional and ethical responsibility with practice teachers to provide appropriate learning environments to prepare students as competent and professional practitioners. The accreditation standards for practice learning require the placement to provide students with regular supervision and exposure to a range of learning strategies, but there is little research that actually identifies the types of placements offering this learning and the key activities provided. This paper builds on an Australian study and surveys social work students in two programs in Northern Ireland about their exposure a range of learning activities, how frequently they were provided and how it compares to what is required by the Northern Ireland practice standards. The results indicated that, although most students were satisfied with the supervision and support they received during their placement, the frequency of supervision and type of learning activities varied according to different settings, year levels and who provided the learning opportunities.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.