Download - IMON DE ONTIGNY H PSWC 2020
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SIMON DE MONTIGNY, PHD
Primer on AI for pharmaceutical scientists
October 2, 2020
PSWC [email protected]
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Disclosure
I have no relationships with commercial interests.
I hold a grant from the Réseau de recherche en santé respiratoire du Québec, a thematic network supportedby the FRQS.
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Research database
Data gathering process of the high resolution database (HRDB) in the Pediatric Intensive Care Unit (PICU) at CHU Sainte-Justine
Brossier et al. Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient. Pediatr Crit Care Med. 2018 Apr;19(4):e189-e198.
>3,000 patients
>1.2x109 data points
The ‘’perpetual patient’’
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Challenges of intensive care data
Compartmentalization Corruption Complexity
Privacy
Integration
Harmonization
Erroneous Data
Missing Data
Imprecise Data
Multimodal Data
State Estimation
Events Prediction
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Validation process of HRDB
Mathieu et al. Validation process of a high‐resolution database in a paediatric intensive care unit—Describing the perpetual patient's validation. J Eval Clin Pract. 2020; 1– 9.
Quantitativevalidation
Reference DatabaseMean difference Agreement R2 ICC
(CI95%)Median [min‐max] Missing data Median [min‐max]
Monitors' data
Heart rate (bpm) 117 [49‐199] 25 (2%) 117 [49‐199] −0.019 99.7% 1 1 (1–1)Respiratory rate (bpm) 28 [11‐89] 8 (1%) 28 [11–89] −0.001 99.9% 1 1 (1–1)Pulse oximetry (%) 100 [74‐100] 10 (1%) 100 [74‐100] 0 100.0% 1 1 (1–1)… … … … … … … …
Ventilators' data
Positive end expiratory pressure (cmH2O)
7 [5‐13] 0 7 [5–13] 0 100% 1 1 (1–1)
Positive inspiratory pressure (cmH2O)
18 [8–35] 0 18 [8–35] −0.022 95.5% 1 1 (1–1)
Respiratory rate (rpm) 34 [11‐56] 0 34 [11–56] 0.008 94% 1 1 (1–1)… … … … … … … …Infusion pumpsRate of infusion (ml/h) 1.3 [0‐100] 23 (9%) 1.3 [0–100] 0 100% 1 1 (1–1)
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Validation process of HRDB
Brossier et al. Qualitative subjective assessment of a high‐resolution database in a paediatric intensive care unit—Elaborating the perpetual patient's ID card. J Eval Clin Pract. 2020; 26: 86– 91.
Qualitative validation
… …
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Patient monitoring
Özcan et al. Alarm in the ICU! Envisioning Patient Monitoring and Alarm Management in Future Intensive Care Units. In: Pfannstiel M., Rasche C. (eds) Service Design and Service Thinking in Healthcare and Hospital Management. Springer, Cham, 2019.
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Patient profilingBrossier et al. Perpetual and Virtual Patients for CardiorespiratoryPhysiological Studies. J Pediatr Intensive Care. 2016 Sep;5(3):122-128.
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AI in the ICU• Applications
– Severity scoring
– Prediction of sepsis
– Decision support in mechanical ventilation
• Benefits
– Rapid analysis of many physiological variables
– Reduce inter-clinician variability
• Challenges
– Fully electronic data (no paper!)
– Data security and privacy
Lovejoy et al. Artificial intelligence in the intensive care unit. Crit Care 23, 7 (2019).
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AI techniques• Recurrent Neural Networks (and variants) for modeling
real time data streams
• Automatized modeling and simulation: System EntityStructure approach to build virtual patients
Rim B et al. Deep Learning in PhysiologicalSignal Data: A Survey. Sensors (Basel). 2020 Feb 11;20(4):969.
Zeigler B et al. Artificial Intelligence in Modeling and Simulation. In: Meyers R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY, 2009.
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Real time analysis
• In a HRDB, even data preprocessing steps can be time consuming
• Algorithm performance is critical
• Example– Synchronization (learning) of
virtual patient with real patient
– Might be needed at differenttimes during an intervention
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Thank you!