Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

An assessment of riparian restoration outcomes in two rural catchments in south-western Victoria: Focusing on tree and shrub species richness, structure and recruitment characteristics

Riparian ecosystems are among the most degraded systems in the landscape,and there has been substantial investment in their restoration. Consequently, monitoring restoration interventions offers opportunities to further develop the science of riparian restoration, particularly how to move from small-scale implementation to a broader landscape scale. Here, we report on a broad range of riparian revegetation projects in two regions of south-western Victoria, the Corangamite and Glenelg-Hopkins Catchment Management Areas. The objectives of restoration interventions in these regions have been stated quite broadly, for example, to reinstate terrestrial habitat and biodiversity, control erosion and improve water quality. This study reports on tree and shrub composition, structure and recruitment after restoration works compared with remnant vegetation found regionally. Within each catchment, a total of 57 sites from six subcatchments were identified, representing three age-classes: <4, 4–8 and >8–12 years after treatment, as well as untreated (control) sites. Treatments comprised fencing to exclude stock, spraying or slashing to reduce weed cover, followed by planting with tube stock. Across the six subcatchments, 12 reference (remnant) sites were used to provide a benchmark for species richness, structural and recruitment characteristics and to aid interpretation of the effects of the restoration intervention. Vegetation structure was well developed in the treated sites by 4–8 years after treatment. However, structural complexity was higher at remnant sites than at treated or untreated sites due to a higher richness of small shrubs. Tree and shrub recruitment occurred in all remnant sites and at 64% of sites treated >4 years ago. Most seedling recruitment at treatment sites was by Acacia spp. This assessment provides data on species richness, structure and recruitment characteristics following restoration interventions. Data from this study will contribute to longitudinal studies of vegetation processes in riparian landscapes of south-western Victoria.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Regulation of HbPIP2;3, a latex-abundant water transporter, is associated with latex dilution and yield in the rubber tree (Hevea brasiliensis Muell. Arg.)

Rubber tree (Hevea brasiliensis) latex, the source of natural rubber, is synthesised in the cytoplasm of laticifers. Efficient water inflow into laticifers is crucial for latex flow and production since it is the determinant of the total solid content of latex and its fluidity after tapping. As the mature laticifer vessel rings are devoid of plasmodesmata, water exchange between laticifers and surrounding cells is believed to be governed by plasma membrane intrinsic proteins (PIPs). To identify the most important PIP aquaporin in the water balance of laticifers, the transcriptional profiles of ten-latex-expressed PIPs were analysed. One of the most abundant transcripts, designated HbPIP2;3, was characterised in this study. When tested in Xenopus laevis oocytes HbPIP2;3 showed a high efficiency in increasing plasmalemma water conductance. Expression analysis indicated that the HbPIP2;3 gene was preferentially expressed in latex, and the transcripts were up-regulated by both wounding and exogenously applied Ethrel (a commonly-used ethylene releaser). Although regular tapping up-regulated the expression of HbPIP2;3 during the first few tappings of the virginal rubber trees, the transcriptional kinetics of HbPIP2;3 to Ethrel stimulation in the regularly tapped tree exhibited a similar pattern to that of the previously reported HbPIP2;1 in the virginal rubber trees. Furthermore, the mRNA level of HbPIP2;3 was associated with clonal yield potential and the Ethrel stimulation response. Together, these results have revealed the central regulatory role of HbPIP2;3 in laticifer water balance and ethylene stimulation of latex production in Hevea.

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.

I'm searching for solutions: why are obese individuals turning to the Internet for help and support with 'being fat'?

INTRODUCTION: This study explores what types of information obese individuals search for on the Internet, their motivations for seeking information and how they apply it in their daily lives. METHOD: In-depth telephone interviews with an Australian community sample of 142 individuals with a BMI ≥ 30 were conducted. Theoretical, purposive and strategic samplings were employed. Data were analysed using a constant comparative method. RESULTS: Of the 142 individuals who participated in the study, 111 (78%) searched for information about weight loss or obesity. Of these, about three quarters searched for weight loss solutions. The higher the individual's weight, the more they appeared to search for weight loss solutions. Participants also searched for information about health risks associated with obesity (n = 28), how to prevent poor health outcomes (n = 30) and for peer support forums with other obese individuals (n = 25). Whilst participants visited a range of websites, including government-sponsored sites, community groups and weight loss companies, they overwhelmingly acted upon the advice given on commercial diet websites. However, safe, non-judgemental spaces such as the Fatosphere (online fat acceptance community) provided much needed solidarity and support. CONCLUSIONS: The Internet provides a convenient source of support and information for obese individuals. However, many turn to the same unsuccessful solutions online (e.g. fad dieting) they turn to in the community. Government and community organisations could draw upon some lessons learned in other consumer-driven online spaces (e.g. the Fatosphere) to provide supportive environments for obese individuals that resonate with their health and social experiences, and address their needs.

LC–MS and GC–MS metabolite profiling of nickel(II) complexes in the latex of the nickel-hyperaccumulating tree Sebertia acuminata and identification of methylated aldaric acid as a new nickel(II) ligand

Targeted liquid chromatography–mass spectrometry (LC–MS) technology using size exclusion chromatography and metabolite profiling based on gas chromatography–mass spectrometry (GC–MS) were used to study the nickel-rich latex of the hyperaccumulating tree Sebertia acuminata. More than 120 compounds were detected, 57 of these were subsequently identified. A methylated aldaric acid (2,4,5-trihydroxy-3-methoxy-1,6-hexan-dioic acid) was identified for the first time in biological extracts and its structure was confirmed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy. After citric acid, it appears to be one of the most abundant small organic molecules present in the latex studied. Nickel(II) complexes of stoichiometry NiII:acid = 1:2 were detected for these two acids as well as for malic, itaconic, erythronic, galacturonic, tartaric, aconitic and saccharic acids. These results provide further evidence that organic acids may play an important role in the transport and possibly in the storage of metal ions in hyperaccumulating plants.