II Artificial Intelligence in Healthcare Symposium and Datathon

4 minute read

From April 25-27th 2019 Hospital Israelita Albert Einstein (HIAE), the PROADI-SUS project and MIT organized its Artificial Intelligence in Healthcare Symposium and Datathon.

In the first day, we attended a Symposium including talks and round tables with experts in the field of Artificial Intelligence, Big Data and Healthcare. I took some notes of things I found interesting and will be posting here for further consultation.


Richard Delaney - Vital Strategies

Intersection Between High-dimensionality Data and Established Public Health Databases

  • High Dimensional Data:
    • Many factors to control for, harder to do hypothesis testing. One alternative is to leverage machine learning to do more pattern recognition
    • Don’t always come with pre conceived ideas, but try to find patterns freely
    • Find probabilities of actions to know where to deliver Public health service
  • Creating conditions to success:
    1. Start with a project: avoid “We prepare and then we act”, we are never prepared enough! Start easy, purposeful, datathon events follow this idea!
    2. After the first step, discuss systemic changes

Nick Guldemond - The Institute of Health Policy & Management Erasmus University

Digital Technologies for Public and Private Primary Care

  • Design Thinking for Health Data Science Projects
    • Patient Journey
    • Clinical Pathway
  • Data new vs old Wikipedia
    • Findable
    • Accessible
    • Interoperable
    • Reusable
  • If we find a good solution, how do we scale? To another region, etc…

Jacson Venâncio Barros - President of the Brazilian Association of CIO’s in Health

Technologies for Supporting Public Health Management and Governance Problem: Startups are disconnected from end users

  • 70% of hospitals don’t have eletronic medical records
  • Lack of standards in vocabulary (taxonomy)
  • Asymmetry of information: each specialty has its own jargon
  • Lack of trust among health sectors Challenge: Identify in this sea of ​​data, which is the drop that interests us

Round Table: Challenges of Big Data and AI in Population Health - Gisele Bohn, Richard Delaney, Nick Guldemond, Jacson Barros, Fatima Marinho


  • Aggregate behavioral data to clinical data
  • Think small first: break problems into small questions that big data can help
  • Predict events that are repeated in certain population groups
  • Integration with the real world
  • How social networks influence behaviour

Open Session: Data Access, Privacy and Security - Andrea Suman, Silvio Pereira, Anderson Soares, Marcelo Felix, Rogéria Leoni Cruz

  • Restrictive law?
  • Big Data x Data Governance
  • No safe culture, territoriality, data sharing (would it restrict the access for research?)
  • Loss of potential of using machine learning

Leonardo Rolim Ferraz

Data Driven ICU - Einstein Initiative

Lucas Bulgarelli - MIT/HIAE

Big Data 360

  • De-identification
  • Leveraging electronic health records for clinical research
  • Sharing - Physionet

Ary Serpa Neto - HIAE

Innovative Models of Clinical Trials Using Large Databases

Christopher Cosgriff - MIT

Deep Learning: A Brief Overview for Clinicians

Round Table: Challenges of Big Data and AI in the Intensive Care Unit - Leo Anthony Celi, Alistair Johnson, Leonardo Rolim Ferraz, Ary Serpa Neto, Lucas Bulgarelli

  • The Book of Why

Leo Anthony Celi - MIT

MIT Experience in Data Analytics applied to Intensive Care Units

  • MIMIC (Medical Information Mart for Intensive Care)
  • Book: Secondary Analysis of Electronic Health Records
  • Opportunities for AI in Healthcare
    • Classification
    • Prediction
    • Optmization (Precision Medicine)

Alistair Johnson - MIT

Keynote: MIMIC Across Modalities: X-rays and Beyond

  • Chest X-rays are ubiquitous, radiologists are not
  • Perfect algorithms on imperfect reports

Matthieu Komorowski - Imperial College London

Keynote: Reinforcement Learning Approaches to Decision Support in Sepsis

Open Session: Opportunities for Innovation in Healthcare - Pedro Marton Pereira, Gustavo Landsberg, Gisele Bohn, André Bem

I forgot to take notes.



Cauê Bueno, Eduardo Casaroto, Fernando Ramos, Gustavo Silveira, Jacqueline Silva,
Matheus Silva, Marcelo Fiorelli, Sonia Altavila, Wellington Araújo

Pulse Pressure: a new outcome predictor in the Intensive Care Unit?

Brief literature review and motivation for study

  • Mean Arterial Pressure (MAP) is the main parameter used to define hemodynamic condition in critically ill patients and levels below 65mmHg are related to poor outcomes especially in septic patients
  • Pulse Pressure (PP) is related to pressure and stroke volume.
  • Pulse Pressure is a neglected hemodynamic parameter at bedside.

Aim of Study

Evaluate if Pulse Pressure is a reliable predictor of 28-days mortality compared to Mean Arterial Pressure in critically ill patients

Data Source

  • Inclusion criteria:
    • First ICU admissions (MIMIC-3)
  • Exclusion criteria
    • Patients readmitted at ICU in the same hospitalization
    • Age < 18 years

Statistical analysis

  1. Description of the sample:
    • mean and standard deviation
    • bars plots and boxplots
  2. Logistic regression model
    • Outcome: 28-days mortality (from ICU admission)
    • Model 1: mean arterial pressure
    • Model 2: pulse pressure


Characteristis of the Sample


SD: standard deviation *Values taken when the minimum systolic blood pressure where observed in the first 24h of ICU admission




Logistic Regression

Outcome = 28-days mortality

Model 1: predictor = mean arterial pressure OR = 0.9694 95% IC = 0.9672 - 0.9717

Model 2: predictor = pulse pressure OR = 0.9960 95% IC = 0.9942 - 0.9977

Discussion and Next Steps

  • MAP assessment alone might not be useful to predict outcome to all ICU patients
  • Preliminary result: PP assessment looks to be a better outcome predictor for “higher PP” patients
  • Promising results: min PP>70 - No min MAP difference between outcomes


Next Steps:

  • Group analysis: who are those patients? Are they elderly people?
  • Check ICU interventions (mechanical ventilation, vasopressors etc)?
  • Should we develop a specific guideline for those patients?