Rate My Professor Rob Free

Free's research centers on developing and translating advanced data science approaches into practical tools for healthcare professionals, with a focus on integrating complex multidisciplinary data sources such as omics data, clinical test results, coded observations, imaging, and mobile health data. Key projects include AI/ML-based models to predict outcomes for community-acquired pneumonia from multifaceted hospital admission data, self-learning digital twins for acute respiratory patients in A&E, and secure AI clinical decision support systems with data validation and monitoring. He has contributed as technical lead and co-investigator to high-impact studies including the East Midlands Breathomics Pathology Node, Extended Cohort for E-health, Environment and DNA (EXCEED), and national pandemic efforts like the UK Research study into Ethnicity and COVID-19 outcomes in Healthcare workers (UK-REACH). Notable publications include 'A data-driven framework for clinical decision support applied to pneumonia management' (Frontiers in Digital Health, 2023), 'Specialist pneumonia intervention nurse service improves pneumonia care and outcome' (BMJ Open Respiratory Research, 2021), 'LabPipe: an extensible bioinformatics toolkit to manage experimental data and metadata' (BMC Bioinformatics, 2020), and 'GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies' (European Journal of Human Genetics, 2014). Free supervises several interdisciplinary PhD students across data-driven AI health analytics, clinical decision support infrastructure, and complex data integration challenges.