Search results for "raw data"
showing 5 items of 35 documents
Improving predictive accuracy of exit polls
2010
Abstract Exit polls are best known for their use in election forecasting. In recent years, however, some prominent mistaken predictions have been made, undermining public confidence in the accuracy of both exit polls and survey methods. Nonresponse bias has been claimed as being one of the main reasons for inaccurate projections. Traditionally, the issue has been handled through an age–race–sex adjustment at the national and state levels. An alternative solution is suggested and detailed in this paper. A two-step strategy is proposed to reduce nonresponse bias and improve predictions. First, “vote-remembering” (vote recall) is used to correct party proportion estimates at polling locations;…
Heart Failure Occurrence: Mining Significant Patterns and 10 Days Early Prediction
2021
Electronic health records containing patient’s medical history, drug prescription, vital signs measurements, and many more parameters, are being frequently extracted and stored as unused raw data. On the other hand, machine learning and data mining techniques are becoming popular in the medical field, providing the ability to extract knowledge and valuable information from electronic health records along with accurately predicting future disease occurrence. This chapter presents a study on medical data containing vital signs recorded over the course of some years, for real patients suffering from heart failure. The first significant patterns that come along with heart failure occurrence are…
Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
2021
Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locali…
SNPs detection by eBWT positional clustering
2019
Sequencing technologies keep on turning cheaper and faster, thus putting a growing pressure for data structures designed to efficiently store raw data, and possibly perform analysis therein. In this view, there is a growing interest in alignment-free and reference-free variants calling methods that only make use of (suitably indexed) raw reads data. We develop the positional clustering theory that (i) describes how the extended Burrows–Wheeler Transform (eBWT) of a collection of reads tends to cluster together bases that cover the same genome position (ii) predicts the size of such clusters, and (iii) exhibits an elegant and precise LCP array based procedure to locate such clusters in the e…
Measuring physical activity with activity monitors in patients with heart failure: from literature to practice. A position paper from the Committee o…
2020
The aims of this paper were to provide an overview of available activity monitors used in research in patients with heart failure and to identify the key criteria in the selection of the most appropriate activity monitor for collecting, reporting, and analysing physical activity in heart failure research. This study was conducted in three parts. First, the literature was systematically reviewed to identify physical activity concepts and activity monitors used in heart failure research. Second, an additional scoping literature search for validation of these activity monitors was conducted. Third, the most appropriate criteria in the selection of activity monitors were identified. Nine activi…