73,512 research outputs found
Adaptive Approach of Data Mining Using HACE Algorithm
Data mining is an interdisciplinary subfield of computer science, is the computational process of discover patterns in large data sets involving methods at the intersection of artificial intelligence , machine learning, statistic, and database systems. Big Data is a new term used to identify the datasets that due to their large size and complication. Data comes from everywhere, sensors used to gather climate information, post to social media sites, digital pictures and videos etc. this data is known as big data. Big Data concern large-volume, difficult, growing data sets with many, independent sources. With the fast development of networking, data storage, and the data group ability, Big Data is now fast expanding in all science and work domains, including physical, biological and bio-medical science. This paper gives brief idea about a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining point of view.
DOI: 10.17762/ijritcc2321-8169.15038
Precision medicine ā A promising, yet challenging road lies ahead
Precision medicine proposes to individualize the practice of medicine based on patientsā genetic backgrounds, their biomarker characteristics and other omics datasets. After outlining the key challenges in precision medicine, namely patient stratification, biomarker discovery and drug repurposing, we survey recent developments in high-throughput technologies and big biological datasets that shape the future of precision medicine. Furthermore, we provide an overview of recent data-integrative approaches that have been successfully used in precision medicine for mining medical knowledge from big-biological data, and we highlight modeling and computing issues that such integrative approaches will face due to the ever-growing nature of big-biological data. Finally, we raise attention to the challenges in translational medicine when moving from research findings to approved medical practices
Editorial: biological ontologies and semantic biology
As the amount of biological information and its diversity accumulates massively there is a critical need to facilitate the integration of this data to allow new and unexpected conclusions to be drawn from it. The Semantic Web is a new wave of web- based technologies that allows the linking of data between diverse data sets via standardised data formats (ābig dataā). Semantic Biology is the application of semantic web technology in the biological domain (including medical and health informatics). The Special Topic encompasses papers in this very broad area, including not only ontologies (development and applications), but also text mining, data integration and data analysis making use of the technologies of the Semantic Web. Ontologies are a critical requirement for such integration as they allow conclusions drawn about biological experiments, or descriptions of biological entities, to be understandable and integratable despite being contained in different databases and analysed by different software systems. Ontologies are the standard structures used in biology, and more broadly in computer science, to hold standardized terminologies for particular domains of knowledge. Ontologies consist of sets of standard terms, which are defined and may have synonyms for ease of searching and to accommodate different usages by different communities. These terms are linked by standard relationships, such as āis_aā (an eye āis_aā sense organ) or āpart_ofā (an eye is āpart_ofā a head). By linking terms in this way, more detailed, or granular, terms can be linked to broader terms, allowing computation to be carried out that takes these relationships into account
Process-oriented Iterative Multiple Alignment for Medical Process Mining
Adapted from biological sequence alignment, trace alignment is a process
mining technique used to visualize and analyze workflow data. Any analysis done
with this method, however, is affected by the alignment quality. The best
existing trace alignment techniques use progressive guide-trees to
heuristically approximate the optimal alignment in O(N2L2) time. These
algorithms are heavily dependent on the selected guide-tree metric, often
return sum-of-pairs-score-reducing errors that interfere with interpretation,
and are computationally intensive for large datasets. To alleviate these
issues, we propose process-oriented iterative multiple alignment (PIMA), which
contains specialized optimizations to better handle workflow data. We
demonstrate that PIMA is a flexible framework capable of achieving better
sum-of-pairs score than existing trace alignment algorithms in only O(NL2)
time. We applied PIMA to analyzing medical workflow data, showing how iterative
alignment can better represent the data and facilitate the extraction of
insights from data visualization.Comment: accepted at ICDMW 201
Big Data Transforms Discovery-Utilization Therapeutics Continuum.
Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
Pages, 1 Figur
Integrating Emerging Areas of Nursing Science into PhD Programs
The Council for the Advancement of Nursing Science aims to āfacilitate and recognize life-long nursing science career developmentā as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2010 American Association of Colleges of Nursing Position Statement āThe Research-Focused Doctoral Program in Nursing: Pathways to Excellence,ā Idea Festival Advisory Committee members focused on emerging areas of science and technology that impact the ability of research-focused doctoral programs to prepare graduates for competitive and sustained programs of nursing research using scientific advances in emerging areas of science and technology. The purpose of this article is to describe the educational and scientific contexts for the Idea Festival, which will serve as the foundation for recommendations for incorporating emerging areas of science and technology into research-focused doctoral programs in nursing
- ā¦