Tuberculous enterocolitis

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A profile of the elderly admitted to the emergency unit of Groote Schuur Hospital : with particular reference to their health care needs

This study is the first of its kind undertaken at Groote Schuur Hospital. It is an attempt to provide a holistic profile of their elderly patients with a view to encouraging further, more specific research, and to provide information for use in the planning of efficient health care for the aged. The study was based on three premises: (i) there is an interrelationship between the ageing process and disease; (ii) a non-disease-specific approach which focusses on the functional status of elderly patients can be used as a predictor of health services consumption; and (iii) any study which promotes understanding of the dynamics of health care of the elderly must also take into account the ageing process and its effect on a particular population within a specific social context. The research spanned 52 weeks (1 March 1989 - 27 February 1990). A sample of nine patients per week was selected from the total population of patients aged 65 and over admitted to the Emergency Unit of Groote Schuur Hospital. Two adult female researchers, using structured questionnaires, constructed in English and comprising subtests, utilising indexes and scales, interviewed respondents and/or household members in their own homes. Data was also obtained from the hospital files. Although essentially descriptive by nature, use was made of groups in regard to variables such as "first admission" (admission to the Emergency Unit), and "readmission" (a previous overnight admission in the preceding year). Statistical analysis, where indicated, was by means of non-parametric tests

MissForest—non-parametric missing value imputation for mixed-type data

Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. Results: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. Availability: The ℝ package missForest is freely available from http://stat.ethz.ch/CRAN/. Contact: [email protected]; [email protected]

Tabanidae

abstract not availabl

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

THE BLOOD-SUCKING ARTHROPODS OF THE DUTCH EAST INDIAN ARCHIPELAGO

abstract not availabl

Causal Stability Ranking

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs. We therefore consider inferring stable rankings of genes, according to their causal effects on a phenotype, from observational data only. Our method allows for efficient design and prioritization of future experiments, and due to its generality it is useable for a broad spectrum of applications

Causal stability ranking

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method that can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time, we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Second, we compared CStaR to established regression-based methods on a gene expression dataset of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications. Availability: The full table of ranked genes, all raw data and an example R script for CStaR are available from the Bioinformatics website. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics onlin