Predicting Heart Failure Patient Events by Exploiting Saliva and Breath Biomarkers Information

The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented

Introducción: teoría y econometría en el análisis histórico

Aquest article és una introducció al present número monogràfic de la Revista d’Història Industrial – Industrial History Review sobre «Teoria i econometria en l’anàlisi històrica ». En primer lloc, revisa breument la bibliografia sobre l’evolució de la història econòmica des de l’anomenada revolució cliomètrica, és a dir, l’ús creixent de la teoria per emmarcar l’evidència històrica i també de tècniques quantitatives, sobretot per estudiar-ne la causalitat. En segon lloc, proporciona una descripció general de com una possible revolució en el tipus i l’ús de les dades, impulsada pel progrés tecnològic en la recopilació i el processament d’informació històrica, pot estar afectant la història econòmica. Finalment, proporciona un breu resum dels articles inclosos en aquest número especial, els quals destaquen de quina manera la teoria econòmica i les noves anàlisis quantitatives poden utilitzar-se per explorar una varietat de temes dins de la disciplina de la història econòmica, que abasten diferents països i períodes de temps.This article introduces the special issue of the Revista de Historia Industrial – Industrial History Review on theory and econometrics in historical analysis. First, it briefly reviews the literature on the evolution of economic history since the so-called cliometric revolution, i.e., the growing use of theory to frame historical evidence and the increasing use of quantitative techniques, particularly to study causality. Secondly, it provides an overview of how a potential data revolution – prompted by technological progress in historical-data collection and processing – may be affecting the field. Lastly, the introduction provides a succinct summary of the articles included in the special issue, which highlight how economic theory and new quantitative analysis can be used to explore a range of issues within economic history, spanning different countries and time periods

A multiscale and multiparametric approach for modeling the progression of oral cancer

Abstract Background In this work, we propose a multilevel and multiparametric approach in order to model the growth and progression of oral squamous cell carcinoma (OSCC) after remission. OSCC constitutes the major neoplasm of the head and neck region, exhibiting a quite aggressive nature, often leading to unfavorable prognosis. Methods We formulate a Decision Support System assembling a multitude of heterogeneous data sources (clinical, imaging tissue and blood genomic), aiming to capture all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse. Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease. For this purpose a Dynamic Bayesian Network (DBN) is employed which is able to capture in a transparent manner the underlying mechanism dictating the disease evolvement, and employ it for monitoring the status and prognosis of the patients after remission. Results By feeding as input to the DBN data from the baseline visit we achieve accuracy of 86%, which is further improved to complete discrimination when data from the first follow-up visit are also employed. Conclusions Knowing in advance the progression of the disease, i.e. identifying groups of patients with higher/lower risk of reoccurrence, we are able to determine the subsequent treatment protocol in a more personalized manner.</p

CAN ANTS PREDICT BANKRUPTCY? A COMPARISON OF ANT COLONY SYSTEMS TO OTHER STATE-OF-THE-ART COMPUTATIONAL METHODS

In the current work, we consider the applicability of Ant Colony Systems (ACS) to the bankruptcy prediction problem. ACS are nature-based algorithms that mimic the functions of live organisms to find the best performing solution. In our work, ACS are used for the extraction of classification rules for bankruptcy prediction. An experimental study was conducted in order to evaluate the performance of the system and identify well performing parameters. Results were compared to the performance obtained by state-of-the-art methods for classification, namely the Artificial Neural Networks, the Support Vector Machines, the Partial Decision Trees and the Fuzzy Lattice Reasoning. Comparison indicates the high performance of the ACS which is further supported by their ability to extract classification rules, thus offering interpretation of the prediction results. The latter is of great importance in the field of corporate distress where no unified theory on distress prediction exists. Most studies with distress prediction have focused on increasing the accuracy of the model and have not always paid attention to the model interpretation.Ant colony systems, rule extraction, support vector machines, neural networks, decision trees, fuzzy lattice reasoning, bankruptcy prediction

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1 Automated Ischemic Beat Classification Using Genetic Algorithms and Multicriteria Decision Analysis

Abstract—Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patient’s age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91 % in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature. Index Terms—Automated ischemia detection, genetic algorithms (GAs), multicriteria analysis. I