Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections

BIR: A Method for Selecting the Best Interpretable Multidimensional Scaling Rotation using External Variables

Interpreting nonlinear dimensionality reduction models using external features (or external variables) is crucial in many fields, such as psychology and ecology. Multidimensional scaling (MDS) is one of the most frequently used dimensionality reduction techniques in these fields. However, the rotation invariance of the MDS objective function may make interpretation of the resulting embedding difficult. This paper analyzes how the rotation of MDS embeddings affects sparse regression models used to interpret them and proposes a method, called the Best Interpretable Rotation (BIR) method, which selects the best MDS rotation for interpreting embeddings using external information

Uncertainty and label noise in machine learning

This thesis addresses three challenge of machine learning: high-dimensional data, label noise and limited computational resources. Learning is usually hard in high-dimensional spaces, due to the curse of dimensionality and other phenomena like the concentration of distances. One can either handle such data with specific tools or try to reduce their dimensionality using e.g. feature selection. The first contribution of this thesis is to study the adequacy of mutual information to select relevant subsets of features. For both classification and regression problems, mutual information is shown to be a sensible criterion for feature selection in most cases. Counterexamples are discussed, where mutual information fails to select optimal features with respect to common error criteria for classification and regression. However, the probability and impact of such failures is also shown to be limited. The second contribution of this thesis is a survey of the label noise literature. Indeed, label noise is an important problem in classification, whose consequences are various and complex. For example, this thesis shows that label noise affects the segmentation of electrocardiogram signals and the results of feature selection. In each case, a new algorithm is proposed to deal with label noise using a probabilistic modelling introduced by Lawrence and Sch"{o}lkopf. Afterwards, a more generic framework is proposed to deal with instances which have a too large influence on learning. This framework is used to robustify several probabilistic learning algorithms. The last contribution of this thesis is the study of large extreme learning machines. Indeed, extreme learning is a recent trend in machine learning which allows learning non-linear models much faster than other state-of-the-art methods. Extreme learning machines are single layer feedforward neural networks whose hidden layer is randomly initialised and not optimised during learning. Only the output weights of such networks have to be optimised, which explains why learning becomes much faster. This thesis shows that when the number of hidden neurons is large, overfitting can be avoided using regularisation. In this case, a new kernel can be defined using extreme learning, which is shown to give good results for both classification and regression problems. This kernel offers a compromise between prediction accuracy and computational needs which can be useful in contexts where computational time is precious.(FSA - Sciences de l'ingénieur) -- UCL, 201