ESICM Datathon: Day 3

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

  1. Autonomic nervous system function and CV regulation is dynamic and hence the data needs to be dynamic and not a constant
  2. Need to pick out the meaningful physiological parameters to feed into the machine learning algorithm
  3. Important to have large open-access databases
  4. 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

The Artificial Intelligence Clinicians learns optimal treatment strategies for sepsis in intensive care

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