ESICM Datathon: Day 2

Session 3: Advanced data analysis Chairs: J. De Waele, A. Girbes

AI & machine learning for clinical predictive analytics Speaker: M. Ferrario

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

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

Data sources

  • Prospective data collection
  • Administrative databases
  • Registries
  • 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

http://giviti.marionegri.it/

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:

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Predictive models may help us to predict patients discharge form the ICU, to predict intracranial pressure increase and acute kidney injury onset.

Predictive models may help us to predict patients discharge form the ICU, to predict intracranial pressure increase and acute kidney injury onset.

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

Limitations

  • 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

Cognitive Informatics in Health and Biomedicine

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:

  • Patient
  • Unit
  • Education
  • Research

Problems and promises of innovation: why healthcare needs to rethink its love/hate relationship with the new

Improving the Electronic Health Record—Are Clinicians Getting What They Wished For?

The tragedy of the electronic health record

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.

General Data Protection Regulation (GDPR)

Privacy in the age of medical big data

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 strategy

    • Failure prevention –> Failure management strategy

    Train your team

    • Simulated systems fail

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)

Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database

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

A Data Quality Assessment Guideline for Electronic Health Record Data Reuse

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.