Artificial intelligence as an analytic approximation to evaluate associations between parental feeding behaviours and excess weight in Colombian preschoolers.

Parental practices can affect children's weight and BMI and may even be related to a high prevalence of obesity. Therefore, the aim of this study was to evaluate the relationship between parents' practices related to feeding their children and excess weight in preschoolers in Bucaramanga, Colombia, using artificial intelligence. A cross-sectional study was carried out between September and December 2017. The sample included preschoolers who attended child development institutions belonging to the Colombian Institute for Family Wellbeing in Bucaramanga and the metropolitan area (n 384). The outcome variable was excess weight and the main independent variable was parental feeding practices. Confounding variables analysed included sociodemographic characteristics, food consumption, and children's physical activity. All equipment for the anthropometric measurements was calibrated. Logistic regression was used to predict the effect of parental practices on the excess weight of the children, and the AUC was used to measure performance. The parental practices with the greatest association with excess weight in the children involved using food to control their behaviour and restricting the amount of food they offered (use of food to control emotions (OR 1·77; 95 % CI 1·45, 1·83; P = 0·034) and encouraging children to eat less (OR 1·22; 95 % CI 1·14, 1·89; P = 0·045)). Childrearing practices related to feeding were found to be an important predictor of excess weight in children. The results of this study represent implications for public health considering this as a baseline for the design of nutrition education interventions focused on parents of preschoolers.

Disease liability prediction from large scale genotyping data using classifiers with a reject option.

Genome-wide association studies (GWA) try to identify the genetic polymorphisms associated with variation in phenotypes. However, the most significant genetic variants may have a small predictive power to forecast the future development of common diseases. We study the prediction of the risk of developing a disease given genome-wide genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but in doubtful situations may reject making a classification. To test the reliability of our proposal, we used the Wellcome Trust Case Control Consortium (WTCCC) data set, comprising 14,000 cases of seven common human diseases and 3,000 shared controls.

Explaining the genetic basis of complex quantitative traits through prediction models.

The functional characterization of genes involved in many complex traits (phenotypes) of plants, animals, or humans can be studied from a computational point of view using different tools. We propose prediction--from the machine learning point of view--to search for the genetic basis of these traits. However, trying to predict an exact value of a phenotype can be too difficult to obtain a confident model, but predicting an approximation, in the form of an interval of values, can be easier. We shall see that trustable and useful models can be obtained from this relaxed formulation. These predictors may be built as extensions of conventional classifiers or regressors. Although the prediction performance in both cases are similar, we show that, from the classification field, it is straightforward to obtain a principled and scalable method to select a reduced set of features in these genetic learning tasks. We conclude by comparing the results so achieved in a real-world data set of barley plants with those obtained with state-of-the-art methods used in the biological literature.

Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples.

OBJECTIVE: Survival probability predictions in critically ill patients are mainly used to measure the efficacy of intensive care unit (ICU) treatment. The available models are functions induced from data on thousands of patients. Eventually, some of the variables used for these purposes are not part of the clinical routine, and may not be registered in some patients. In this paper, we propose a new method to build scoring functions able to make reliable predictions, though functions whose induction only requires records from a small set of patients described by a few variables. METHODS: We present a learning method based on the use of support vector machines (SVM), and a detailed study of its prediction performance, in different contexts, of groups of variables defined according to the source of information: monitoring devices, laboratory findings, and demographic and diagnostic features. RESULTS: We employed a data set collected in general ICUs at 10 units of hospitals in Spain, 6 of which include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501, 19.83% of whom did not survive. Using these data, we report a comparison between the SVM method proposed here with other approaches based on logistic regression (LR), including a second-level recalibration of release III of the acute physiology and chronic health evaluation (APACHE, a scoring system commonly used in ICUs) induced from the available data. The SVM method significantly outperforms them all from a statistical point of view. Comparison with the commercial version of APACHE III shows that the SVM scores are slightly better when working with data sets of more than 500 patients. CONCLUSIONS: From a practical point of view, the implications of the research reported here may be helpful to address the construction of cheap and reliable prediction systems in accordance with the peculiarities of ICUs and kinds of patients.

Genetical genomics: use all data.

BACKGROUND: Genetical genomics is a very powerful tool to elucidate the basis of complex traits and disease susceptibility. Despite its relevance, however, statistical modeling of expression quantitative trait loci (eQTL) has not received the attention it deserves. Based on two reasonable assertions (i) a good model should consider all available variables as potential effects, and (ii) gene expressions are highly interconnected, we suggest that an eQTL model should consider the rest of expression levels as potential regressors, in addition to the markers. RESULTS: It is shown that power can be increased with this strategy. We also show, using classical statistical and support vector machines techniques in a reanalysis of public data, that the external transcripts, i.e., transcripts other than the one being analysed, explain on average much more variability than the markers themselves. The presence of eQTL hotspots is reassessed in the light of these results. CONCLUSION: Model choice is a critical yet neglected issue in genetical genomics studies. Although we are far from having a general strategy for model choice in this area, we can at least propose that any transcript level is scanned not only for the markers genotyped but also for the rest of gene expression levels. Some sort of stepwise regression strategy can be used to select the final model.