Haralampos Karanikas

Making sense of big data in health research: Towards an EU action plan

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

A Pattern Similarity Scheme for Medical Image Retrieval

In this paper, we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the PAtterns for Next generation DAtabase systems (PANDA) framework for pattern representation and management. The proposed scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form higher-level, semantically meaningful patterns. The clustering of the feature space is realized by an expectation-maximization algorithm that uses an iterative approach to automatically determine the number of clusters. Then, the 2-component property of PANDA is exploited: the similarity between two clusters is estimated as a function of the similarity of both their structures and the measure components. Experiments were performed on a large set of reference radiographic images, using different kinds of features to encode the low-level image content. Through this experimentation, it is shown that the proposed scheme can be efficiently and effectively applied for medical image retrieval from large databases, providing unsupervised semantic interpretation of the results, which can be further extended by knowledge representation methodologies.

Visual techniques for the interpretation of data mining outcomes

The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing a new 3-Dimensional visual data mining technique for the representation and mining of classification outcomes and association rules. Categories: I.2.4, I.2.6 Research Paper: Data Bases, Work Flow and Data mining

MetaOn - Ontology Driven Metadata Construction and Management for Intelligent Search in Text and Image Collections

State of the art in multimedia technology focuses in managing data collected from various sources, including documents, images, video, and speech. Therefore the effective management, analysis and mining of such heterogeneous data require the combination of various techniques. In this paper, we present an overview of the funded MetaOn project. The core objective of MetaOn is to construct and integrate semantically rich metadata collections extracted from documents, images and linguistic resources, to facilitate intelligent search and analysis. The proposed MetaOn framework involves ontology-based information extraction and data mining, semi-automatic construction of domain specific ontologies, content-based image indexing and retrieval, and metadata management. The Hellenic history has been chosen as a challenging application case study

Erratum to: Making sense of big data in health research: towards an EU action plan

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. European healthcare systems and the potential for big data Medicine has traditionally been a science of observation and experience. For thousands of years, clinicians have integrated the knowledge of preceding generations with their own life-long experiences to treat patients according to the oath of Hippocrates; mostly based on trial and * Correspondence: cauffray@eisbm.org; rudi.balling@uni.lu European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010 Paris, France Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch-sur-Alzette, Luxembourg Full list of author information is available at the end of the article

Erratum to: Making sense of big data in health research: Towards an EU action plan [Genome Med., 8 (2016) (71)]

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. European healthcare systems and the potential for big data Medicine has traditionally been a science of observation and experience. For thousands of years, clinicians have integrated the knowledge of preceding generations with their own life-long experiences to treat patients according to the oath of Hippocrates; mostly based on trial and * Correspondence: cauffray@eisbm.org; rudi.balling@uni.lu European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010 Paris, France Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch-sur-Alzette, Luxembourg Full list of author information is available at the end of the article

Correction: Making sense of big data in health research: towards an EU action plan (vol 8, pg 71, 2016)

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. European healthcare systems and the potential for big data Medicine has traditionally been a science of observation and experience. For thousands of years, clinicians have integrated the knowledge of preceding generations with their own life-long experiences to treat patients according to the oath of Hippocrates; mostly based on trial and * Correspondence: cauffray@eisbm.org; rudi.balling@uni.lu European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010 Paris, France Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362 Esch-sur-Alzette, Luxembourg Full list of author information is available at the end of the article

Discovering market trends in the biotechnology industry

In this paper we describe our approach to discover trends for the biotechnology and pharmaceutical industry based on temporal text mining. Temporal text mining combines information extraction and data mining techniques upon textual repositories and our main objective is to identify changes of associations among entities of interest over time. It consists of three main phases; the Information Extraction, the ontology driven generalisation of templates and the discovery of associations over time. Treatment of the temporal dimension is essential to our approach since it influences both the annotation part (IE) of the system as well as the mining part.