The Dynamical Analysis Laboratory (DAL) is a research lab led by Dr. Andrew Seely at the Ottawa Hospital Research Institute (OHRI), affiliated with the University of Ottawa and The Ottawa Hospital.

Our vision is to improve care through continuous monitoring of multiorgan variability. Our work focuses on complexity science, variability analysis, statistical analyses, applied machine learning and clinical decision support systems.

The DAL has over a decade of experience facilitating the acquisition of waveform data from major physiological monitoring equipment vendors. Our expertise includes the cleaning, quality assessment and processing of waveform data in order to extract variability and complexity patterns over time, via custom developed software such as Continuous Individualized Multi-organ Variability Analysis (CIMVA) and waveform viewing and annotation software.

The DAL leverages the variability and complexity information along with clinical, laboratory, metabolic or environmental data into decision support solutions to clinical problems. In addition, we embed these solutions into prototype tools that can be used at the bedside, including development and management of custom clinical databases, real-time polling of relevant data from clinical servers, calculation of specific outcome scores and generation of reports.

The DAL team includes highly experienced biomedical engineers, clinical research program manager, technical writer, informatics expert and long-time local collaborators with expertise in statistics, epidemiology, neurophysics, pediatrics, kinesiology and critical care medicine. Moreover, the DAL has developed strong collaborations with a wide range of clinical scientists, academic researchers, as well as industrial partners. The DAL holds multiple patents on the use of variability in clinical decision support systems.

Current DAL projects reflect our philosophy of personalized bedside care, using the variability of physiological waveform acquired at the bedside and a variety of machine learning techniques to derive clinical decision support tools and help clinicians make informed, timely decision at the bedside.

Research at the DAL