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In recent years, the introduction of data analytics to large amounts of healthcare data collected on daily basis opened numerous new opportunities and challenges in the field of medical informatics. By definition, healthcare informatics refers to the process of leveraging information technologies to improve the quality of healthcare. Many researchers are focusing on basic and translational research to achieve this goal by proposing novel or applying and adapting the state-of-the-art data analytics techniques to vast amounts of recently collected data. Recent adoption of Electronic Health Records (EHR) opens additional opportunities for data analytics, as we are able to access structured and unstructured data that is systematically collected for each event in the healthcare system or even contributed by the patients themselves. This tutorial covers different data analytics techniques and their translational value in improving the quality of healthcare. In the introductory part of the tutorial, we will outline the basics of data analytics in healthcare and continue with description of data representation that is specific to this field. The second part of the tutorial will present concrete state-of-the-art approaches that can be applied in healthcare informatics. Participants will gain a better understanding of risk estimation and stratification, patient similarity, privacy-preserving predictive modelling and patient-based classification. All methods presented in the tutorial have great translational value and can be implemented as a stand-alone solution or a part of health information systems. The intended audience of this tutorial are healthcare professionals and researchers from all fields of healthcare informatics. No specific knowledge will be required since the tutorial is self-contained and most fundamental concepts will be introduced during the presentation.

Rapid advancements in biotechnologies such as -omic (genomics, proteomics, metabolomics, lipidomics etc.), next generation sequencing, bio-nanotechnologies, molecular imaging, and mobile sensors etc. accelerate the data explosion in biomedicine and health wellness. Multiple nations around the world have been seeking novel effective ways to make sense of "big data" for evidence-based, outcome-driven, and affordable 5P (Patient-centric, Predictive, Preventive, Personalized, and Precise) healthcare. My main research focus is on multi-modal and multi-scale (i.e. molecular, cellular, whole body, individual, and population) biomedical data analytics for discovery, development, and delivery, including translational bioinformatics in biomarker discovery for personalized care; imaging informatics in histopathology for clinical diagnosis decision support; bionanoinformatics for minimally-invasive image-guided surgery; critical care informatics in ICU for real-time evidence-based decision making; and chronic care informatics for patient-centric health. In this talk, first, I will highlight major challenges in biomedical and health informatics pipeline consisting of data quality control, information feature extraction, advanced knowledge modeling, decision making, and proper action taking through feedback. Second, I will present informatics methodological research in (i) data integrity and integration; (ii) case-based reasoning for individualized care; and (iii) streaming data analytics for real-time decision support using a few mobile health case studies (e.g. Sickle Cell Disease, asthma, pain management, rehabilitation, diabetes etc.). Last, there is big shortage of data scientists and engineers who are capable of handling Big Data. In addition, there is an urgent need to educate healthcare stakeholders (i.e. patients, physicians, payers, and hospitals) on how to tackle these grant challenges. I will discuss efforts such as patient-centric educational intervention, community-based crowd sourcing, and Biomedical Data Analytics MOOC development. Our research has been supported by NIH, NSF, Georgia Research Alliance, Georgia Cancer Coalition, Emory-Georgia Tech Cancer Nanotechnology Center, Children's Health Care of Atlanta, Atlanta Clinical and Translational Science Institute, and industrial partners such as Microsoft Research and HP.

Most common and complex diseases, such as diabetes and cancer, are influenced at some level by variation in the genome. To truly address the goal of translational research, genetic variation must be taken into consideration. Research done in public health genetics, specifically in the area of single nucleotide polymorphisms (SNPs), is the first step to understanding human genetic variation. In addition, novel methods are needed to represent and to conduct text mining over textual genotypic data sources. In this paper, we describe the development and evaluation, in the context of a genetic study, of a translational-informatics method that supports both machine-learning text mining (e.g., Conditional random fields) and automated inference for identifying key concepts (e.g., Hypotheses and results). After scaling for inter-annotator agreement, our adjusted overall precision was 64%, with a range of 48% to 80%. While other biological text mining systems have focused on named-entity recognition, the development of tools for genetic studies focusing on hypotheses and results has been relatively rare.

Every piece of information learned in human health has the potential to improve the length and quality of life for patients. However, the search for more knowledge in the domain of Health Informatics has led to vast quantities of data, far more than can be easily processed by researchers. Fortunately, Justas this problem of Big Data has begun to challenge advances in Health Informatics, so too can the data mining and machine learning techniques used for the general study of Big Data be brought to bear on the these problems. This paper will discuss recent studies done on Big Data Analytics in the field of Health Informatics which sought to answer various clinical questions, using data acquired from the molecular, tissue, and patient levels of Health Informatics. We also consider what work remains to be done in this area.

The following topics are dealt with: BigLS workshop; next-generation sequencing and assembly; metagenomics and metaproteomics; systems biology and informatics; network science and analytics; statistical and integrative approaches; big data biology and informatics; scientific workflows and platforms; and high performance computing tools and challenges.

The use of highly active anti-retroviral therapy (HAART) and other supportive treatments for Human immunodeficiency Virus (HIV) are changing research on malignancies associated with HIV-infected patients, both for Acquired Immune Deficiency Syndrome (AIDS)-defining malignancies (ADM) as well as non-AIDS-defining malignancies (nADM). At the same time, studies linking HIV/AIDS registries to cancer registries have shown potential for exploring new approaches to prevention and treatment of opportunistic disease. We take an innovative approach to semantic modeling using data curation and interactive representation of the semantically aggregated information, using biostatistical analysis and annotation tools as well as geographic information systems technology. Our hypothesis is that, by investigating the application of innovative translational informatics methods and creating prototypes of collaborative knowledge bases, we can facilitate novel clinical research related to the identification of patterns in current and new data, linking the integrated information and outcomes to available registries.

In the current Information age, progress of Biomedical Sciences requires successful integration with Computational and Information Sciences. This track seeks to demonstrate how such integration is being achieved via Bioinformatics and Biomedical Informatics research. The focus of this track is on the implementation of data mining tools and subsequent data analysis in biomedical research and highlights the current state of research in bioinformatics and medical informatics. The two papers of this mini-track focus on specific informatics methods for improvement of health care on various levels, however, the message to be taken from these papers is the real potential for advancement involving the integration of medicine and informatics.

The paradigm of the emerging Personalized Health Informatics (PHI) area looks to be driven by several factors. Some of them are Country independent. Some others highly depend from the way the specific Country organizes its healthcare system. Nevertheless, several architectural visions and practical applications may take positive inputs from the view cared on each other side of the ocean. This paper presents outlines from EU-US experiences in PHI.

We review our efforts in the Bioinformatics of Computational Functional Anatomy where we compute functional and physiological response variables in anatomical coordinates. Collaborative efforts in brain and heart imaging in NIH funded initiatives such as the Biomedical Informatics Research Network (BIRN) Morphometry project and the Carido Vascular Research Network (CVRG) respectively are discussed.

Medical informatics is a highly interdisciplinary field of research, where a great deal of collaboration is needed to achieve efficient solutions. We are witnessing a remarkable proliferation of biomedical knowledge on one side and the revolution of communication technologies on the other. In this manner we may expect that a proper methodology to support collaboration strategies of medical informatics researchers and research teams based on knowledge, should improve the collaboration and the creativity in the field of medical informatics research. Based on the required functionalities of such a system, we propose the semantic web as the underlying technology and explain its use for knowledge sharing both within an organization and inter-organizationally.

Further progress of Biology and Medical Sciences requires successful integration with Computational and Information Sciences. The papers of this Mini-track provide a diverse spectrum of innovative ways of how such integration can be achieved. The focus of this mini-track is on the development and implementation of advanced data management tools in Biomedical Informatics. The first three papers in the mini-track focus on the development and implementation of computational and IT tools for Biomedical Informatics, while the forth paper presents advanced statistical methods for data analysis.

Computer Infrastructure for Systems Biology is the first international conference session dedicated to systems biology informatics held at the 18th International Conference on Systems Engineering. Our goal is to bring forth ideas and collaborations among industrial and academic bioinformaticians, biocomputing professionals, data analysts, and system biologists to facilitate systems biology research and findings. The compilation of twelve full session papers and four poster papers at this meeting represents a snapshot of emerging research and development trends in systems biology informatics.

Due to recent advances in health information science and technology coupled with ever increasing need for education in biomedical informatics, health professions educators and biomedical teaching systems developers are faced with both a wealth of opportunity and formidable educational challenge. This paper presents an overview of a multidisciplinary approach to health professions education in biomedical informatics.