Title: Workshop on Brain Network Analysis
Presenters: Maggie Cheng (Illinois Institute of Technology)
Overview: The workshop will focus on the study of brain networks. The topics of the workshop include both biomedical signal processing and big data analytics/machine learning, with a necessary addition in connectomics. The modelling, analysis and inference on brain activities from a complex network approach is complementary to the study of neuro-informatics. The workshop will solicit research presentations that address computational models and analytical tools, as well as the use of large-volume, high-dimensional experimental data. Both mathematical modelling and machine learning/artificial intelligence will be covered. The theme of the workshop is consistent with the theme of BHI-2019. It is relevant to two conference tracks, and yet not fully covered by any of the tracks. Therefore, it is necessary to propose a workshop specializing in brain network analysis.
Title: Women in the Biomedical Engineering Workforce: inspiring new leaders and professional development
Presenters: Maria Teresa Arredondo (Universidad Politécnica de Madrid); Holly Jimison (Northeastern University)
Overview: The goal of this workshop is to stimulate, through the view of different professionals in the Biomedical Engineering research arena, advancing women in the workplace. Women in biomedical and health informatics (BHI) face significant challenges in developing their careers, especially in leadership roles. A workshop focused on women’s career development provides important information and resources and helps build a community of women leaders and mentors to support professional growth of our future leaders. The intended audience includes women at all stages in their careers, from students to seasoned veterans, as well as anyone who wishes to mentor women in BHI.
Title: Data Analytics in HealthCare / Tutorial
Presenters: Themis Exarchos (Dept. of Informatics, Ionian University); Vasileios Pezoulas (Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina)
Overview: This workshop will present new trends on data analytics in healthcare, while it will discuss the significant challenges occurring when managing and analysing large amounts of data from different sources. Data curation, data harmonization, as well as, ethical, legal and related restrictions associated with the sharing and pooling of individual-level information will be also discussed. Current techniques, ranging from data standardization to advanced ontology and semantic interlinking technologies that enable the creation of semantic links among diverse datasets, will be presented. Additional emphasis will be given on distributed machine learning algorithms that deal with the analysis of data from disparate medical data sources. Several case studies will be highlighted related to the pooling, curation, harmonization and analysis of healthcare data. Considering that new technologies have not been holistically validated and evaluated yet, the present workshop will contribute positively to the adoption of new practices in data analytics in healthcare.
Title: Modelling in bioengineering and bioinformatics
Presenters: Nenad Filipovic (University of Kragujevac)
Overview: Today the treatment in the medicine still relies exclusively on diagnostic imaging data to define the present state of the patient, biomarkers and experience of the medical doctors to evaluate the efficacy of prior treatments for similar patients. Computational methods, big data analytics, machine learning, artificial intelligence, bioinformatics, give opportunity for a patient-specific model in order to improve the quality of prediction for the disease progression into life-threatening events that need to be treated accordingly. In this special Workshop authors will present with advanced research support tools for disease characterization, and the integrative informatics; associations among heterogeneous data, that can improve the predictive power of the patient specific model.