Data Science Superhero

Dr. Ashwin Belle of MCIRCC supercharges his powers of prediction

Become a Data Science Superhero!
DataRobot

Get the guide to "Moving from BI to Machine Learning with Automation"

Download free ebook
DataRobot

See how other data science superheroes acquired their powers

View On-Demand Webinar
DataRobot

Take the first step toward becoming a Data Science Superhero

Sign up for a live demo

Dr. Ashwin Belle is an analytics architect on the Data Science team at the Michigan Center for Integrative Research in Critical Care (MCIRCC), one of the nation’s most innovative organizations in diagnosing, monitoring, and treating patients with critical illness or injury and a part of the University of Michigan’s Michigan Medicine Health System. MCIRCC is comprised of several teams, faculty members, and physicians who are focused on using interdisciplinary collaboration to discover new and better ways to diagnose, detect, and treat medical problems with technology.


Become a Data Science Superhero!
“The reason I wake up every day and come to work is to be able to build and develop analytic solutions that can save lives,” said Ashwin.

The field of medicine is awash in data from doctor visits, diagnostic tests, and routine patient monitoring by machines and nurses alike, presenting a huge opportunity for data scientists like Ashwin. Even though machines are constantly monitoring patients, long-term patterns often go unrecognized due to the fleeting nature of doctors’ rounds and nurse visits and the sheer volume of data that is produced. The challenge lies in capturing and prioritizing which pieces of data are relevant.



MCIRCC is working to harness this data to supercharge their life-saving work by applying data science superpowers to critical care situations.

Become a Data Science Superhero!

MCIRCC’s Data Science division is an integral part of their strategy to use big data to discover ways to better mitigate critical medical situations like internal bleeding. The first step was finding a way to capture all the relevant data and store it, which required the development of a special infrastructure. Once that monumental task was complete, Ashwin and his team had to figure out the best way to apply data science to use that data to inform clinical decision-making and improve outcomes for patients.


Ashwin and his team knew there were opportunities to use data science to save lives, but were hampered by the complex and time-consuming nature of traditional data science methods.

The data science team at MCIRCC were experiencing issues such as:

  • — Overly complex models took too much time to develop and implement
  • — Models and methods weren’t easily scalable to adjust to the vast amounts of data being produced by medical monitoring systems
  • — Models didn’t provide a high enough degree of accuracy or allow for the use of real-time data
  • — A communication gap existed between data scientists, engineers, and medical professionals


After seeing a DataRobot demo, Ashwin saw a possible solution and immediately activated the DataRobot signal.

Become a Data Science Superhero!

Impressed by the speed and accuracy of DataRobot’s automated machine learning platform, not to mention the platform’s ability to deliver multiple options for different predictive models, Ashwin knew DataRobot would provide the tools MCIRCC needed to take their lifesaving applications of data science to the next level.

“DataRobot has removed that time burden of machine learning, which has really enhanced my mission of trying to save lives by increasing the number of possible predictive models I’m able to develop,” said Ashwin.
Become a Data Science Superhero!

With DataRobot, MCIRCC and Ashwin can now:

  • Reduce model development and implementation time from one to two months to only two days, dramatically increasing the number of predictive models they can test and implement
  • Use advanced data exploration capabilities in DataRobot to discover which data points are relevant to the problem at hand
  • Combine the use of DataRobot and advanced signal and image processing methodologies to effectively implement predictive solutions

Ashwin and the rest of the team are already using DataRobot to address a number of predictive challenges at MCIRCC. One project with life-saving potential is a model that predicts the possibility of Hemodynamic Instability in intensive care patients. The model evaluates thousands of data points produced by medical devices that monitor patients in real time, alerting hospital staff when the onset of hemodynamic compromise is detected well before traditional vital signs. If testing goes well and the model proves accurate in assisting physicians, the next step is to leverage commercial partnership to achieve FDA approval in order to scale the model to save lives across the country.

Data Science Superhero vs Mortal
Data Science Superhero vs Mortal
Traits & Capabilities
Traits & Capabilities Data Scientist Mortal Data Scientist Superhero
Enterprise Readiness
Bottom Line Business Impact
Bottom Line Business Impact $100K+ per year $M's+ / Year
Model Engine Tested
Model Engine Tested No 200,000,000+ times
Model Sharing
Model Sharing No Yes
Enterprise Readiness
Enterprise Readiness Manual Automatic
Security
Security Manual Automatic
Standardization
Standardization When there's time Every time
Multitasking
Multitasking Single model Multiple models
Package installation and dependency management
Package installation and dependency management Manual Not needed
Risk of coding errors
Risk of coding errors Substantial None
Model Accessibility
Popularity with business users
Popularity with business users
 Somewhat Very
Business user engagement
Business user engagement
 Too busy Always available


Helped with guardrails
Helped with guardrails None Yes
Skills Needed
Skills Needed Significant - PhD-level
 PhD not required
Model Operationalizing
Model deployment effort
Model deployment effort Significant Effortless
Model Quality
Model iteration
Model iteration
 Manual & slow Automated
Accuracy
Accuracy Time consuming Guaranteed
Helped by other Data Scientists
Helped by other Data Scientists No 85+
Workflow
Workflow Manual Automatic
Tuning
Tuning Manual Automatic
Ensembled models
Ensembled models Manual Automatic
Algorithms and libraries
Algorithms and libraries A comfortable few Many
Feature engineering
Feature engineering
 Manual Automatic

Model Quantity
Number of models / month
Number of models / month
 Very Few
 Thousands
Speed
Speed Sometimes Blisteringly Fast
Model Transparency
Model Transparency
Model Transparency None X-Ray Vision
Explainable Models
Explainable Models
 'Black Box'
 Automatic

To be continued…

In the future, not only will DataRobot help people like Ashwin develop predictive algorithms that save lives, but because DataRobot is built to allow non-data-scientists to understand results, and reasons for its predictions, the platform will help MCIRCC speed up the time it takes for its solutions to get through clinical trials.

“The easier it is for us to explain to the FDA how we did certain things and what we have done, the better. The deployment mechanisms that DataRobot has provided and the direction they are headed is really going to make it easier to get our solutions approved, and thereby save more lives in the process.”

Ashwin Belle

Ashwin Belle

Analytics Architect at MCIRCC

Become a Data Science Superhero!

DataRobot

Get the guide to "Moving from BI to Machine Learning with Automation"

Download free ebook
DataRobot

See how other data science superheroes acquired their powers

View On-Demand Webinar
DataRobot

Take the first step toward becoming a Data Science Superhero

Sign up for a live demo