A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on average behaviors, and thus, they do not take into account the particular response to those drugs by different patients, although that response may vary enormously from one patient to another and even for the same patient in different stages of the anemia. This work proposes an advance with respect to previous works that have faced this problem using different methodologies (Machine Learning (ML), among others), since the diversity of the CKD population has been explicitly taken into account in order to produce a general and reliable model for the prediction of ESA/Iron therapy response. Furthermore, the ML model makes use of both human physiology and drug pharmacology to produce a model that outperforms previous approaches, yielding Mean Absolute Errors (MAE) of the Hemoglobin (Hb) prediction around or lower than 0.6 g/dl in the three countries analyzed in the study, namely, Spain, Italy and Portugal.

Optimization of anemia treatment in hemodialysis patients via reinforcement learning.

OBJECTIVE: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. METHODS: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. RESULTS: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.

Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.

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 patients. For the prediction of Hb, both analytical measurements and medication dosage of patients suffering from chronic renal failure (CRF) are used. Two kinds of models were trained, global and local models. In the case of local models, clustering techniques based on hierarchical approaches and the adaptive resonance theory (ART) were used as a first step, and then, a different predictor was used for each obtained cluster. Different global models have been applied to the dataset such as Linear Models, Artificial Neural Networks (ANNs), Support Vector Machines (SVM) and Regression Trees among others. Also a relevance analysis has been carried out for each predictor model, thus finding those features that are most relevant for the given prediction.

Visual data mining with self-organising maps for ventricular fibrillation analysis.

Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healthy patients) and AHR (other anomalous heart rates and noise). A total number of 27 variables were obtained from continuous surface ECG recordings in standard databases (MIT and AHA), providing information in the time, frequency, and time-frequency domains. self-organising maps (SOMs), trained with 11 of the 27 variables, were used to extract knowledge about the variable values for each group of patients. Results show that the SOM technique allows to determine the profile of each group of patients, assisting in gaining a deeper understanding of this clinical problem. Additionally, information about the most relevant variables is given by the SOM analysis.

Matlab-based interface for the simultaneous acquisition of force measures and Doppler ultrasound muscular images.

This paper tackles the design of a graphical user interface (GUI) based on Matlab (MathWorks Inc., MA), a worldwide standard in the processing of biosignals, which allows the acquisition of muscular force signals and images from a ultrasound scanner simultaneously. Thus, it is possible to unify two key magnitudes for analyzing the evolution of muscular injuries: the force exerted by the muscle and section/length of the muscle when such force is exerted. This paper describes the modules developed to finally show its applicability with a case study to analyze the functioning capacity of the shoulder rotator cuff.