0000000000309780
AUTHOR
Rafael Magdalena-benedito
Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques
HighlightsDifferent prediction algorithms were used to predict Hb levels in CRF patients.Prediction errors in the validation cohorts of patients were around 0.6g/dl.Difficulty to obtain lower errors due to the measuring machine precision (0.2g/dl).Relevance analysis of features have been applied for each predictor. Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of …
Foetal ECG recovery using dynamic neural networks
Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coe…
Use of Reinforcement Learning in Two Real Applications
In this paper, we present two sucessful applications of Reinforcement Learning (RL) in real life. First, the optimization of anemia management in patients undergoing Chronic Renal Failure is presented. The aim is to individualize the treatment (Erythropoietin dosages) in order to stabilize patients within a targeted range of Hemoglobin (Hb). Results show that the use of RL increases the ratio of patients within the desired range of Hb. Thus, patients' quality of life is increased, and additionally, Health Care System reduces its expenses in anemia management. Second, RL is applied to modify a marketing campaign in order to maximize long-term profits. RL obtains an individualized policy depe…
Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach
The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treatment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm for the treatment and long-term management of the chronic disease. Reinforcement Learning (RL) can be useful to extract such strategies from clinical data, taking into account delayed effects and without requiring any mathematical model. In this work, we focus on the so-called Fitted Q Iteration algorithm, a RL approach that deals with the data very…
Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques
Penicillium fungi are among the main defects that may affect the commercialization of citrus fruits. Economic losses in fruit production may become enormous if an early detection of that kind of fungi is not carried out. That early detection is usually based either on UltraViolet light carried out manually. This work presents a new approach based on hyperspectral imagery for defect segmentation. Both the physical device and the data processing (geometric corrections and band selection) are presented. Achieved results using classifiers based on Artificial Neural Networks and Decision Trees show an accuracy around 98%; it shows up the suitability of the proposed approach.
Regularized extreme learning machine for regression problems
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…
Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal☆
This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are also other variants which allow to find an optimal architecture, but they tend to need a long time for training, especially in the case of complex data sets. Another relevant contribution of the paper is the new visualization of the patterns in the hierarchical structure. Results show that GHSOM is a powerful and versatile tool to extract relevant …