This research theme spans different fields varying from the local, regional, to the global Public Health, Life Sciences. and Medical Care.
It covers data analysis of medical records through Image Processing as well as Natural Language Processing and the use of Computational Linguistics and Graph Theory applied, and not exclusively, to preprocess and annotate Arabic and medical documents. Additional examples in the analysis of datasets and image processing explore the associations of biomarkers and clinical outcomes for Risk Assessment, and the analysis of MRI for the identification of predictive markers in Multiple Sclerosis and other Neurological Diseases.
This area also covers Health Informatics and Demographics such as the spatial distribution of the Arab region population, medical schools, and healthcare professionals. Health information and Road Traffic Injury (RTI) are also subject fields of this broad theme
Further, Maternal Health Improvement (GAIN MHI) is a focus area where Gamification and Artificial Intelligence for personalization are deployed through mobile applications to improve the quality of antenatal care services in Lebanon, in addition to the use of Machine Learning coupled to Wearable devices for healthy living.
Below is a list of selected faculty members who conduct research relevant to this area
Computational Physiology with emphasis on computational neuroscience, Neural network modeling, and Dynamical Systems Analysis.
Exploring the contribution of spatial epidemiology and spatial data science to public health informatics and public health practice.
Developing novel automated reasoning resources and techniques applied to problems in program correctness and language understanding.
Fatima Abu Salem
Advanced machine learning techniques to model imbalanced datasets with a healthcare outcome.
Global Health Institute
Applying data science to global health issues, including COVID-19.
Computational biosciences and bioinformatics.
Machine Learning Models for Health including mental and phsyical health monitoring with wearable sensors and prediction models for 3D imaging such as CT scans and MRI.
Associations of biomarkers and clinical outcomes - including longitudinal analysis (survival and mixed effects modeling).
Using machine learning to improve health outcomes on a large dataset of 300,000 coupled mothers & newborns.
Medical Record ( image and text) analysis, Wearables for healthy living.
Computational Genomics with emphasis on non-coding DNA and gene regulatory networks.
Biomedical devices and sensor data analytics.
Integrating Quantum Computing with Machine Learning and Natural Language Processing, especially Arabic, to build more intelligent Agents that can be used in Medicine and Robotics.
MRI/CT image assessment and processing (volumetry, atrophy, diffusion tensor imaging ...) through automatic and semiautomatic approaches.
Health Analytics and visualization to assess, reduce and control Injuries.
Bayesian modelling to evaluate the cost-effectiveness of health care interventions, Modelling preference-based health state utility data, measuring and valuing health and quality of life.
Gamification, Artificial Intelligence, and mHealth Network for Maternal Health Improvement (GAIN MHI) [led by the Global Health Institute]. Exploring whether a Mobile Application that uses Artificial Intelligence for personalization will improve the quality of antenatal care services in Lebanon.
Deep learning for Medicine and Public Health.