Selected Research Areas of George Mason Health Informatics Faculty
Health informatics faculty at George Mason University conduct original research in several areas related to health informatics, health information technology, and health services research. Particular research areas of interest are electronic/personal medical records, intelligent systems, health care terminologies, data and text mining, consumer health informatics, clinical decision support, and health data privacy and security. Below are brief descriptions of some of these research areas. For details or information about our expertise in other areas, please contact the department.
Health Informatics Research Expertise
Health informatics faculty, students and affiliates at George Mason University conduct original research in several areas related to health informatics, health information technology, and health services research. Particular research areas include electronic/personal medical/health records, intelligent systems, health care terminologies, data and text mining, consumer health informatics, mobile health, telemedicine, clinical decision support, and health data privacy and security. Below are brief descriptions of some of these research areas. For details about our expertise in other areas, please contact the individual faculty.
Undergraduate and graduate students from the health informatics program and across the university are engaged in various research projects conducted in our program exposing them to world-class research.
Health Data Analytics. Data analytics is the future of health and health care. It is no longer possible for people to navigate the complexities of health and medical disciplines without data analytics. Our faculty interests span a wide range of topics including comparative effectiveness, causality, knowledge discovery from data, predictive modeling, and anomaly detection. In data analytic efforts, we closely collaborate with experts in health services research, health administration, and health policy. Our faculty have expertise with analyzing a wide range of health-related data including clinical (EHR), administrative (claims), survey, and sensor data. Beyond data science and analytics tools, our faculty have deep understandings of the datasets, their structure limitations, and uses.
Faculty: Dr. Farrokh Alemi, Dr. Sanja Avramovic, Dr. Janusz Wojtusiak, Dr. Hong Xue
Machine Learning and Artificial Intelligence. Recent advances in computing and intelligent systems allow for what has not been previously possible in health care. Our faculty and students are engaged in development of machine learning and artificial intelligence methods specifically designed for healthcare problems. Some examples of applications of the methods include prediction of patient outcomes, treatment selection, and location prediction. Our focus is on developing and using transparent methods that clinicians and managers can trust.
Faculty: Dr. Farrokh Alemi, Dr. Sanja Avramovic, Dr. Janusz Wojtusiak
Causality and Bayesian Reasoning with Medical Data. Research in this area is focused on mediation and causal analysis using network models. Recent advances include use of LASSO regression to specify network models, use of stratification of parents in Markov Blanket of high dimensional data, and methods of sequencing temporal events to reflect order of cause and effects.
Faculty: Dr. Farrokh Alemi
Read more: https://pubmed.ncbi.nlm.nih.gov/?term=%22Alemi+F%22+AND+%22causal+analysis%22&size=200
Electronic Health Records. Our faculty and students investigate interrelated areas of research related to electronic health records, personal health records, and electronic medical records. The research spans usability, interoperability, and integration of systems. Specific areas include decision support and intelligent capabilities of systems. Our program helps maintain electronic health record systems used by population health clinics and the School of Nursing.
Faculty: Dr. Farrokh Alemi, Dr. Sanja Avramovic, Dr. Hua Min, Dr. Janusz Wojtusiak
Biomedical Ontologies, Standards, and Medical Coding. The ability to resolve semantic conflicts between heterogeneous information systems is one of the major challenges in the data integration field. Our research aims to achieve semantic interoperability across systems using standards and biomedical ontologies. Our faculty have expertise in data standards, interoperability, and medical coding.
Faculty: Dr. Hua Min
Public and Population Health Informatics. Health informatics plays an important role in organizing and maintaining public health information systems and population health. Our faculty interests include using informatics tools for epidemiology, population-level outcome modeling, and studies of health disparities.
Faculty: Dr. Janusz Wojtusiak, Dr. Hong Xue
Consumer Health Informatics, Mobile Health, and Telemedicine. Developing and evaluating digital health interventions for various underserved populations and bridging health disparities. Digital tools include mobile apps, interactive web portals, social media, Internet of Things (IoT), geospatial tracking, and wearable sensors.
Faculty: Dr. Alicia Hong, Dr. Janusz Wojtusiak, Dr. Hong Xue
Technology Adoption and Health Care Management. Research in this area seeks to understand the factors that affect technology adoption. The focus is mainly on healthcare organizations and the users, mostly healthcare professionals, of new health information technologies. The research draws from different fields, such as communication science and management, to understand factors that impede technology adoption in healthcare settings.
Faculty: Dr. Alicia Hong
Decision Support Systems and Clinical Decision Tools. Research focuses on decision models under mixed uncertainty: risk, ambiguity and ignorance. For many complex decisions especially in health care, the quantification of relevant uncertainty in terms of probability is unreliable and even impossible. We aim at constructing decision support systems that integrate intelligent systems with prediction capabilities and focus on end-users.
Faculty: Dr. Farrokh Alemi, Dr. Alicia Hong, Dr. Janusz Wojtusiak
Informatics program response to COVID-19 pandemic. With uncertain and dynamically changing situation during COVID-19 pandemic, our faculty undertook several projects that aimed at understanding the condition and its impacts. Some of the completed projects are differential diagnosis of COVID-19 vs. flu, study of social distancing and people movements, technology-based contact prediction/tracing, and vaccination scheduling.
Faculty: Dr. Farrokh Alemi, Dr. Sanja Avramovic, Dr. Janusz Wojtusiak
Health Informatics Research Resources
The Health Informatics Learning Lab (HILL) is a dedicated lab and teaching space intended for research and collaboration for informatics students, researchers and faculty. The lab provides unique playground for informatics for students for their in-class and out-of-class projects and is located in Peterson Family Health Sciences.
The Center for Discovery Science and Health Informatics (DSHI) provides computing infrastructure to the health informatics program and the entire College of Public Health. The center hosts a number of research datasets and provides dedicated tools used in health informatics research. Resources are available to health informatics students, faculty and staff.
The Machine Learning and Inference Laboratory (MLI) focuses on developing machine learning methods specifically designed to work with complex data found in medical, health care, and health applications and produce human-oriented results.
Health Informatics Research Resources
The Health Informatics Learning Lab (HILL) is a dedicated lab and teaching space intended for research and collaboration for informatics students, researchers and faculty. The lab provides unique playground for informatics for students for their in-class and out-of-class projects and is located in Peterson Family Health Sciences Hall – the new home of the health informatics program.
The Center for Discovery Science and Health Informatics (DSHI) conducts basic and applied research on developing computational theories, analytic methods, and software applications that support decision making and discovery of knowledge from healthcare data. This includes data mining, artificial intelligence and other knowledge discovery methods and tools tailored toward the meaningful use of health data, health services research, evidence-based practice, and decision support for a variety of health system stakeholders and end-users (clinicians, managers, researchers, policymakers, and consumers) from all sectors of the health system. The center provides computing infrastructure to the College of Public Health, and hosts a number of research datasets.
The Machine Learning and Inference Laboratory (MLI) focuses on developing machine learning methods specifically designed to work with complex data found in medical, health care, and health applications in order to produce human-oriented results. Simply speaking, machine learning is about enabling intelligent systems to perform tasks that are too complex to be programmed. MLI is one of the longest existing labs focused on machine learning with history dating back to the early 1970s.