Session 3: Advanced data analysis Chairs: J. De Waele, A. Girbes
AI & machine learning for clinical predictive analytics Speaker: M. Ferrario
The combination of genomics, metabonomics coupled with Artificial Intelligence and Machine Learning is an incredible one. BUT its application is still an open challenge.
Given the complexity and heterogeneity of the data, there are no well-defined set of procedures to interrogate them.
BUT THERE ARE ISSUES WITH MACHINE LEARNING – Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
New meaning to observational studies Speaker: S. Finazzi
- Prospective data collection
- Administrative databases
- Electronic health records
Research question that can be tackled
- Evaluation of quality of care
- Study clinical and decision making processes
- Analyse pathophysiological phenomena
Besides MIMIC/PhysioNet, there are other collaborative databases out there
These data have been used to improve care and processes in participating departments. Also can act as benchmarking exercise.
The big issue is the quality of the data!
Predictive models and clinical support Speaker: G. Meyfroidt
Examples of application:
Medical data science 101 Speaker: M. Komorowski
Why should we conduct secondary analysis of EHR?
- RCT results not always applicable to real life patients
- RCTs are negative!
- RCTs won’t allow precision medicine
- Not using the data is unethical
- Observational data: difficult to examine causality
- Availability of the data?
- Data quality
Matt then did a LIVE demo on how to build a machine learning model – follow my thoughts here
State of the art of EMRs in Europe Speaker: T. Kyprianou
Adverse effects in medicine: easy to count, complicated to understand and complex to prevent.
There needs to be a shift in focus from error intolerance to error recognition and recovery.
The data for EHR are driven by 4 sources:
Opportunities for ICU CIS/PDMS
- Direct link/real time updates of patient’s medical records
- Healthcare professionals access to all information and services they need in one place
- Patients/family-centric decision-making based on best clinical evidence
- Improve data quality and analysis
- Development of better and more effective security protocols
- Faster test turnaround times to provide quicker diagnosis for patients.
GDPR and pseudonymization Speaker: D. Fulco, A. Di Stasio
This was an absolutely fascinating insight into the GDPR from a legal prospective.
I really think that GDPR is a good thing.
Blackout: when IT fails Speaker: C. Hinske
*Great title slide*
3 types of failure
- Failure to use (e.g. IT blackout)
- Failure to support (e.g. incorrect information)
- Failure to enable (e.g. too much information)
Top 3 tips
- Risk assessment
- Contingency plan where you tolerate workflow disruption (with strict time limit) followed by a fallback plan
- Failure prevention –> Failure management strategy
- Simulated systems fail
Train your team
Prediction and deep learning Speaker: A. Ercole
If you were blown away by Matt’s SQL and Python prowess, wait till you see Ari’s demo. I was mesmorised when he did his party trick at LIVES2016 in Milan.
This time around, he constructed mortality prediction models in real time using R (here)
Another mesmorising site he introduced us to was the Neural Network Playground
*TOP TIP FROM ARI – IF YOU WANT TO LEARN R/PYTHON/SQL, DOWNLOAD THE PACKAGE AND USE IT
The issue of data quality Speaker: S. Vieira
The types of missing data
- Missing at completely random e.g. loss of label in lab test
- Missing at random e.g. arterial pH, PaCO2 measurements in blood
- Missing Not at Random e.g. blood counts which doctor decides not to do
Harmonization of data sources Speaker: B. Illigence
This is a fascinating insight into the process in Germany introducing a national EHR (it’s not completed yet)
Making sense of a big data mess Speaker: H. Hovenkamp
From the founder of PACMED (https://pacmed.ai//) based in Amsterdam
Once upon a time: the story of MIMIC Speaker: R. Mark
This is probably my highlight of day 2. The story of how the MIMIC database came into being from Prof R Mark. Amazing and inspirational. A call for further collaboration. Furthermore, if you use the MIMIC data and publish your research, you must submit your code to an open repository.