1. Artificial Intelligence
Dr. Hawken is involved in several research projects focused on applications of artificial intelligence in maternal, newborn, and pediatric health. In collaboration with Dr. Mark Walker, he is co-leading a suite of projects evaluating deep learning model interpretation of prenatal ultrasound as an early screening tool for fetal anomalies and pregnancy complications. He received funding from CIHR as nominated principal applicant to develop deep learning models for prediction of pre-eclampsia from prenatal placental imaging. He has also formed research collaborations with Investigators at Stanford University to co-develop and externally validate AI/ML risk prediction models for perinatal outcomes in Ontario and California databases.
Selected Publications
• Dick K, Kaczmarek E, … Hawken S, Walker MC, Armour CM. Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data. Sci Rep. 2025 Apr 7;15(1):11816. doi: 10.1038/s41598-025-90216-8.
• Kaczmarek E, Miguel OX, … Hawken S, Armour CM, Walker MC, Dick K. CAManim: Animating end-to-end network activation maps. PLoS One. 2024; 19(6):e0296985. doi: 10.1371/journal.pone.0296985.
• Miguel OX, Kaczmarek E, Lee I, … Hawken S, Armour CM, Dick K, Walker MC. Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis. Sci Rep. 2024; 14(1):9013. doi: 10.1038/s41598-024-59248-4.
• Dick K, Humber J, Ducharme R, … Hawken S, Walker MC. The Transformative Potential of AI in Obstetrics and Gynaecology. J Obstet Gynaecol Can. 2024; 46(3):102277. doi: 10.1016/j.jogc.2023.102277.
• Walker MC, Willner I, Miguel OX, … Hawken S, Aviv RI. Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester. PLoS One. 2022; 17(6):e0269323. doi: 10.1371/journal.pone.0269323.
• Carrington, A. M., Manuel, D. G., Fieguth, P. W., Ramsay, T., Osmani, V., Wernly, B., Bennett, C., Hawken, S., Magwood, O., Sheikh, Y., McInnes, M., & Holzinger, A. Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. IEEE transactions on pattern analysis and machine intelligence. 2023; 45(1): 329-341.
2. Advanced predictive modelling using metabolomic and clinical data
Dr. Hawken served as the lead Biostatistician in a global gestational age prediction research program with Dr. Kumanan Wilson, funded by the Bill & Melinda Gates Foundation. After initial success using existing data from Ontario, ongoing support totalling over $2M USD was secured to expand the scope of work internationally with partner institutions in the US, China, the Philippines, Bangladesh, Zambia and Kenya.
Selected Publications:
• Hawken S, Ducharme R, Murphy MSQ, Olibris B, Bota AB, Wilson LA, et al. Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers. PLoS One. 2023;18(3):e0281074. doi: 10.1371/journal.pone.0281074.
• Hawken S, Ward V, Bota AB, Lamoureux M, Ducharme R, Wilson LA, et al. Real world external validation of metabolic gestational age assessment in Kenya. PLOS Glob Public Health. 2022; 2(11):e0000652. doi: 10.1371/journal.pgph.0000652.
• Hawken S, Ducharme R, Murphy MSQ, Atkinson KM, Potter BK, Chakraborty P, Wilson K. (2017). Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada. BMJ Open. 7(9): e015615.
• Wilson K, Hawken S, Potter BK, Chakraborty P, Walker M, Ducharme R, Little J. (2016). Accurate prediction of gestational age using newborn screening analyte data. American Journal of Obstetrics and Gynaecology. 214(4): 513 e1-9.
3. Vaccine Safety and self-controlled study designs
Dr. Hawken has published extensively on the safety and coverage of publicly funded pediatric vaccines using linked health-administrative databases from ICES and CIHI. He is an expert in applying self-controlled case-series and self-controlled risk interval study design methodology to vaccine safety studies.
Selected Publications:
• Hawken S, Ducharme R, Fell DB, Oron, AP, Wilson K. (2019). Effects of sex and birth weight on nonspecific health services use following whole-cell pertussis vaccination: a self-controlled case series analysis. Human Vaccines & Immunotherapeutics. 2019 Mar 4: 1-6.
• Hawken S , Ducharme R , Rosella LC , Benchimol EI , Langley JM , Wilson K , Crowcroft NS, Halperin SA , Desai S , Naus M , Sanford CJ , Mahmud SM , Deeks SL. (2017). Assessing the risk of intussusception and rotavirus vaccine safety in Canada.Human Vaccines & Immunotherapeutics. 13(3): 703-710.
• Hawken S, Potter BK, Little J, Benchimol EI, Mahmud S, Ducharme R, Wilson K. (2016). The use of relative incidence ratios in self-controlled case series studies: an overview. BMC Med Res. 16(1): 126.
• Hawken S , Kwong JC , Deeks SL , Crowcroft NS , McGeer AJ , Ducharme R , Campitelli MA, Coyle D, Wilson K. (2015). Simulation study of the effect of influenza and influenza vaccination on risk of acquiring guillain-barré syndrome. Emerging infectious diseases. 21(2): 224-31.
• Hawken S, Potter BK, Benchimol EI, Little J, Ducharme R, Wilson K. (2014). Seasonal variation in rates of emergency room visits and acute admissions following recommended infant vaccinations in Ontario, Canada: a self-controlled case series analysis.Vaccine. 32(52): 7148-53.
• Hawken S, Kwong JC, Deeks SL, Crowcroft NS, Ducharme R, Manuel DG, Wilson K. (2013). Association between birth order and emergency room visits and acute hospital admissions following pediatric vaccination: a self-controlled study. PloS one. 8(12): e81070.
• Hawken S , Manuel DG , Deeks SL , Kwong JC , Crowcroft NS , Wilson K. (2012). Underestimating the safety benefits of a new vaccine: the impact of acellular pertussis vaccine versus whole-cell pertussis vaccine on health services utilization. American journal of epidemiology. 176(11): 1035-42.
