John Mark Ansermino


Research Classification

Research Interests

Global Health and Emerging Diseases
Health information systems
Biomedical Technologies
Technological Innovations
Artificial Intellegence
Automation in healthcare
Global Health
Mobile Health
Outcome prediction
Physiological Monitoring
Precision Health
Sepsis in children

Relevant Thesis-Based Degree Programs



J Mark Ansermino is a researcher and clinician in the Department of Anesthesiology, Pharmacology & Therapeutics at the University of British Columbia. He is a Michael Smith Foundation for Health Research Scholar and a Principal Investigator at the Research Institute at British Columbia’s Children’s Hospital. He is Director of the Centre for International Child Health. He co-leads the influential Digital Health Innovation Lab (DHIL) - a research team of engineers and clinicians who are developing and evaluating novel mobile health applications to improve the health outcomes of women and children around the world. As a team, they combine science and engineering to create cutting-edge technology that uses clinical data, automation and smart physical sensors to extract important data features. Their goal is to provide frontline healthcare workers in low and middle-income countries around the world with the key tools they need to make informed medical decisions for their patients.

Research Methodology

Implementation science
Decision support
Digital solutions
Global Health


Postdoctoral Fellows
Any time / year round

Global health

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Monitoring sleep and sleep breathing disorders using pulse oximeter photoplethysmogram (2018)

We developed novel algorithms for monitoring sleep, sleep breathing disorder (SBD)and instantaneous respiratory rate (IRR) in children using the characterization ofpulse oximetry photoplethysmogram (PPG). To evaluate the algorithms, we recordedthe oxygen saturation (SpO₂) and PPG signals from 160 children using a phone-basedoximeter consisting of a microcontroller-based pulse oximeter module interfacinga smartphone. This mobile oximeter was further developed to perform allprocessing on the smartphone through the audio interface.We evaluated the relative impact of SBD on sympathetic and parasympatheticactivity in children through the characterization of PPG and concluded that sympatheticactivity was higher in 30-second epochs with apnea/hypopnea event(s). Welater characterized the SpO₂ pattern in SDB and then combined SpO₂ pattern characterizationand PPG analysis to design a model with two binary logistic classifiersto identify the epochs with apnea/hypopnea events.We developed a novel model for identifying the cycles of random eye movement(REM) and non-REM of the overnight sleep based on the activity of cardiorespiratorysystem using the overnight PPG. We extracted the features associated withpulse rate variability (PRV), respiratory rate (RR), vascular tone and movementfrom PPG to build a model with two binary classifiers to identify wakefulness fromsleep (wake/sleep classifier) and REM from non-REM sleep (non-REM/REM classifier).We also developed a novel algorithm for extracting the instantaneous respiratoryrate (IRR) from PPG. The algorithm was performed in three steps: extractionof three respiratory-induced variation signals from PPG, estimation of IRR fromeach extracted respiratory-induced variation signal and fusion of IRR estimates. A time-frequency transform called synchrosqueezing transform (SST) was usedto extract the respiratory-induced variation signals from PPG. Later, a second SSTwas applied to estimate IRR from respiratory-induced variation signals. To fuseIRR estimates, a novel algorithm was proposed.This study would expand the functionality of conventional pulse oximetry beyondthe measurement of heart rate and SpO₂ to monitor sleep, to screen SBDs andmeasure the respiratory rate continuously and instantly.

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The fullPIERS risk prediction model for women with pre-eclampsia: external validation, recalibration and added value of a novel biomarker (placental growth factor) (2017)

The hypertensive disorders of pregnancy (HDPs), including pre-eclampsia, complicate up to 10% of pregnancies and are leading causes of maternal and perinatal morbidity and mortality. The fullPIERS model was developed to identify and quantify the risks of developing complications for women with pre-eclampsia in high-resource settings and to aid clinicians in managing such pregnancies. Prior to introducing the model into clinical practice, it is important to assess its external validity. Recalibration, if required, and addition of new biomarkers may be helpful to improve the predictive performance of the model. The objectives of this thesis were (i) to assess the external validity of the fullPIERS risk prediction model for women with pre-eclampsia (ii) recalibrate the model if necessary, and (iii) to assess the incremental value of adding the biomarker, placental growth factor (PlGF), to the model.Using abstracted medical records of women admitted into tertiary units in four high-income countries (HICs), the fullPIERS model was assessed for geographical and temporal validity. The model’s predictive ability in women with a broader spectrum of disease including early-onset pre-eclampsia, other HDPs and low and middle-income countries was also assessed using existing cohorts. Good performance was interpreted based on discrimination (AUROCs ≥0.7) and calibration (slope ≥ 0.7). Stratification and classification accuracy of the model were also assessed. The fullPIERS model showed good discriminatory performance on temporal and geographical validity (AUROCs >0.8) and also in broader HDPs (AUROCs >0.7). Medium to high likelihood ratios were estimated (>5 to >10) at a predicted probability cut-off of ≥30% for ruling in adverse maternal outcomes. Calibration was reduced in all cohorts (
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