Dr. Johnson is a Scientist at SickKids Research Institute. He received his Bachelor of Biomedical and Electrical Engineering at McMaster University and successfully read for a DPhil at the University of Oxford. Dr. Johnson has worked in machine learning and health care for over a decade. Throughout his career, Dr. Johnson has strived to break down barriers in medical informatics, and his work on MIMIC-III demonstrates the immense potential of publicly available data. His research interests include retrospective observational research, machine learning, and new approaches to modeling heterogenous clinical data.
Dr. Johnson runs the Knowledge in Data (KinD) lab in Child Health Evaluative Sciences program at SickKids Research Institute. The lab focuses on retrospective observational studies to generate new knowledge, predictive modeling using machine learning to prognosticate outcome, and curating data to support the research enterprise. Dr. Johnson’s research interests include machine learning, natural language processing, and critical care.
Education and Experience
- 2014: DPhil, Healthcare Innovation, University of Oxford, Oxford, UK
- 2009: BEng, Biomedical & Electrical Engineering, McMaster University, Hamilton, ON
- 2020–Present: Scientist, Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON
- 2020–Present: Faculty Affiliate, Vector Institute, Toronto, ON
- 2017–2020: Research Scientist, Massachusetts Institute of Technology, Cambridge, MA, USA
- 2015–2017: Postdoctoral Associate, Massachusetts Institute of Technology, Cambridge, MA, USA
- 2013: Visiting Researcher, Emory University, Atlanta, GA , USA
- 2010: Visiting Researcher Cerner Corporation, Vienna, VA, USA
- 2019: Design, Innovate, Impact Challenge Winner, Track 3
- 2018: Forbes 30 under 30 – Healthcare
- 2018: SCCM 47th Annual Congress Star Research Award
- 2017: SCCM 46th Annual Congress Star Research Award
- 2013: PCinC Challenge on Multimodal peak detection, 1st Prize
- IET Healthcare Technologies Network – William James Award
- 2012: PCinC Challenge on Mortality prediction 2012. 1st Prize
- RCUK Digital Economy Programme – CDT Postgraduate Studentship
- Bulgarelli, L, Deliberato, RO, and Johnson, AE. Prediction on critically ill patients: The role of “big data”. Journal of Critical Care 2020.
- Cai, Z, Li, J, Johnson, AE, et al. Rule-based rough-reﬁned two-step-procedure for real-time premature beat detection in single-lead ECG. Physiological Measurement 2020.
- Bose, S, Johnson, AE, Moskowitz, A, Celi, LA, and Raﬀa, JD. Impact of intensive care unit discharge delays on patient outcomes: a retrospective cohort study. Journal of intensive care medicine 2019;34:924–929.
- Deliberato, RO, Escudero, GG, and Johnson, AE et al. SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries. International journal of medical informatics 2019;131:103959.
- Deliberato, RO, Neto, AS, and Johnson, AE et al. An evaluation of the inﬂuence of body mass index on severity scoring. Critical care medicine 2019;47:247–253.