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The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

Tutorial on
From Early Childhood to Aging: How to use AI, Graph Models and Mobility Data to Advance Healthcare?


Hesham Ali
College of Information Science and Technology, University of Nebraska at Omaha
United States
Brief Bio
Hesham H. Ali is a Professor of Computer Science and the director of the University of Nebraska Omaha (UNO) Bioinformatics Core Facility. He served as the Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at UNO between 2006 and 2021. He has published numerous articles in various IT areas, including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to model complex biomedical systems and analyze big bioinformatics data.

The last several years we witnessed major advancements in the development of sensor technologies and wearable devices with the goal of collecting various types of data in many application domains. We also have significantly higher computational resources and advanced informatics tools, including complex networks and AI tools. Although these developments are welcomed, there is so much left to be done to take full advantage of the available data gathered. The most critical missing component is the lack of advanced and targeted data analytics. In this tutorial, we attempt to address this challenge by presenting new data analytic tools that connect mobility and heath. We utilize available AI tools in processing the collected data, and then introduce new graph modeling and complex networks to analyze the processed data.
We discuss how to employ population-based algorithms in order to implement the proposed approaches. We demonstrate how the new tools can be applied to analyze all types of mobility and medical data to reveal useful health-related features that can be used to improve healthcare in case studies related to early childhood development, developmental disorders, and aging research. We also utilize graph-theoretic mechanisms to zoom in and out of the network models and extract different types of information at various granularity levels to uncover more knowledge. The proposed approach paves the way towards a new decision support system that leads to new discoveries in biomedical research and healthcare applications.


- Mobility for Health
- Aging Research
- Early Childhood Development
- Developmental Disorders
- AI Tools
- Graph Models and Complex Networks
- Preventive Healthcare

Aims and Learning Objectives

The fields of Biomedical Informatics and big data analytics have been attracting a lot of attention in recent years, particularly with the emergence of powerful AI tools. The use of wireless devices to collect various types of critical mobility and medical data continues to grow-- both in the commercial world, as well as in the research domain. The impact of such devices remains limited though, primarily due to the lack of sophisticated data analytics tools to allow for the extraction of useful information out of the collected data. The proposed tutorial will address these issues with a particular focus on the following objectives:
1 - Provide an overview of available wearable devices and the types of mobility and medical data such devices can collect, as well as discuss current research studies associated with the use of mobility and medical data in biomedical research and healthcare.
2 - Introduce the main ideas associated with obtaining mobility patterns or signatures using raw data collected from wearable devices and use AI filtering tools to identify the key mobility/medical parameters needed to assess the health level of individuals.
3 - Introduce the basic concepts related to using correlation and similarity networks to store, visualize and analyze data associated with different applications in the biomedical informatics domain and show the potential of using these networks as a key component of an advanced decision support system for next-generation healthcare.
4 - Introduce the participants to how graph models and complex networks can be developed using mobility and medical data to assess health levels for various groups. The main goal of the proposed models is to classify health levels, track their health variability pattern, and predict potential health hazards.
5 - Show how the proposed approach can be used specifically for the early diagnosis of health issues associated with early childhood development, developmental disorders, and aging conditions.

Target Audience

The tutorial is intended primarily for computational scientists who are interested in wireless networks and data analytics, and their application in the biomedical domain. It is also of interest to Biomedical and Engineering researchers since the focus of the main application domains of the proposed methodology is health informatics and engineering. In particular, the tutorial targets researchers interested in how mobile devices and wireless technologies can be used to support the new direction of healthcare which is focused on predictive and preventative approaches. Biomedical scientists and engineers with some background in computational concepts, who are interested in how new technologies can support health care and medical information systems, represent another intended audience.

Prerequisite Knowledge of Audience

Basic background in computer science and mobile devices would be helpful but not necessary. The main concepts will be introduced in a highly accessible manner.

Detailed Outline

The tutorial focuses on the use of mobile devices and mobility data in health monitoring and assessment; introducing the concepts of mobility signatures that are identified using data collected from wireless sensors; using correlation networks and graph theoretic tools to properly analyze mobility data and assess health levels; and studying how correlation networks can be used to link mobility studies with biomedical research. The tutorial will also address how to use AI approaches, clustering algorithms and advanced graph theoretic tools to provide big data analysis of the collected data used to build the correlation networks, and how to integrate different types of data, including mobility data and genetic information, in order to provide a comprehensive analysis for health data for individuals.
1. Survey of current wireless technologies in healthcare - Brief discussion on the various research studies and commercial wireless devices developed with the goal of monitoring health activities and the measurement of various mobility parameters.
2. How to obtain mobility signatures using raw mobility data – Algorithms for classifying various daily activities, using mobility data, will be introduced and used to build the characterizing models for a mobility signature. Such characterizing patterns can be used to accurately measure the level of mobility associated with each individual.
3. Big data analytics using correlation networks – New techniques for building correlation networks from sensor data collected from groups will be presented. Big Data analysis tools will be introduced to analyze the developed correlation networks and predict health levels of various cases with a focus on predicting potential health problems.
4. How to use Mobility to assess healthcare – Correlation Networks for modeling and integrating various types of data will be presented. The integration model represents potential next steps in healthcare in which various types of data will be used to establish an accurate picture associated with each person’s health.
5. How to apply the proposed approach in several case studies – how to improve healthcare related to childhood development, developmental disorders, and aging research will be presented.

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