Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72

This paper considers whether neural networks might be used to analyse firm activity and the evolution of industries. The key findings of the simulation results used are summarised as follows. While efficiency seeking behaviour has growth advantages, compared to unchanged firms, these are small compared to the growth advantages that are displayed with firms that are able to exploit input use variability. In addition the two sectors analysed here (UK SIC34 and SIC72) show different profit implications of these growth advantages. In SIC34 an increase in firm growth caused by strategic flexibility coincides with an increase in profitability, whereas in SIC72 the increase in firm growth coincides with a profitability reduction. This difference is explained in terms of the differing market structures in the two sectors along with the differing effects of market shocks. Finally the market structure effects of differing firm types have been analysed. It is shown that factor flexibility generates relative growth advantages that benefit smaller firms. But strategic flexibility generates relative growth effects that benefit larger firms

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure

Using simple neural networks to analyse firm activity

Characteristically, in economics, the analysis of firm activity is based on a production function that defines a deterministic relationship between factor inputs and firm output. The analysis of the firm as an organisation takes a somewhat different approach. For instance, behavioural economics (for example Simon, 1955; March and Simon, 1958; Cyert and March, 1963), transaction cost theory (Williamson, 1975, 1985) and capabilities approaches (for example Foss and Loasby, 1998; Foss, 2005) emphasise that economic agents have inevitably incomplete information and knowledge and are at most boundedly or limitedly rational. The implication here is that while general principles governing intra-firm interaction can be specified, detailed organisational processes inside the firm are, for practical academic purposes, effectively unobservable. Hence, the usual analytical tools designed to analyse firm behaviour, based on production functions and optimising principles with full information, are in practice an oversimplification of firm activity (Loasby, 1999)

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation