Hemodynamics in septic shock Speaker: G. Baselli
Amazing how a ‘routine’ haemodynamic system can be viewed so differently from the point of view of an engineer/data scientist/medic
In conclusion
- Autonomic nervous system function and CV regulation is dynamic and hence the data needs to be dynamic and not a constant
- Need to pick out the meaningful physiological parameters to feed into the machine learning algorithm
- Important to have large open-access databases
- These databases need to integrate multi-scale information both in dimension and in time
Neurointensive care Speaker: A. Ercole
The concept of cerebral perfusion pressure = MAP – ICP is an example of a simple mathematical concept
We measure what we can, NOT what we should
Perhaps the autoregulation status of the TBI pt is more important – cerebrovascular pressure reactivity (PRx) Link
Data studies needs the same robustness as any other drug studies
Data Access quality and Curation for Observational Research Designs
Reinforcement learning in sepsis Speaker: M. Komorowski
Can a computer help the clinician do the right thing?
Looked at 2 groups of treatment in septic patients – vasopressor use and fluid administration.
In general pts received more IV fluids and less vasopressors than recommended by the AI policy.
The machine considered much more variables compared to the human clinician
Conclusion
- Reinforcement learning could provide clinically interpretable treatment suggestions
- The models could improve outcomes in sepsis
- Flexible framework transferable to other clinical questions
Gradient-boosted decision trees Speaker: C. Cosgriff
“Its not about doctors versus computers, its doctors with computers versus doctors without computers – @cosgriffc”
If you don’t know what gradient boost is (I don’t) but would like to find out more OR want to learn some basic coding in R/Python/SQL, have a look at kaggle.com
The power of XGBoost for the ICU is to shift towards ‘human intelligence’, supporting clinicians and intensivists (NOT REPLACING THEM)
References:
- Introduction to Statistical Learning
- Elements of statistical learning
- Deep Learning with Python
- xgBoost: A scalable tree boosting system
Clinical research and AI Speaker: R. Furlan
This is highlighted by a local project looking at the ability to diagnose syncope using natural language algorithms of EHR compared to human clinicians
Moving models to the bedside Speaker: P. Thoral
Insight into the challenges on bringing machine learning and artificial intelligence models to the bed side.
Highlights the various legislative requirement with regards to introducing system (medical software is still considered a medical device)
Need to engage the stakeholders
The future: physicians, engineers, machines Speaker: R. Barbieri
Various scenarios of the future predicted by Prof Barbieri
Armageddon
- Technology used in warfare
- Technology cannot overcome environmental disasters
Best Scenario
- Technology cures, predicts and prevents diseases
- No need for ICUs
Idiocracy
- Technology blunts human knowledge
- Humans lose ability to think
- Technology takes over but are unable to make critical decisions
Oligarchy (aka BladeRunner)
- Technology and knowledge controlled by a few
- Progress without common wealth
- Humans and machines start merging
Terminator
- Machines takes over
- Humans and human nature are irrelevant
Optimistic
- New age on enlightenment
- The SMART ICU
