Machine Learning Platform at Mount Sinai Health Systems


Arash Kia
Data Science Lead
Mount Sinai
Prem Timsina
Lead Data Engineer
Mount Sinai

Overview

Prem Timsina and Arash Kia are part of a collaborative data science team at Mount Sinai whose goal is to bring the power of machine learning to clinicians in an effort to best serve patients. In this presentation, they speak to how their team comprised of mathematicians, devops engineers, data engineers, data scientists, and clinicians have created prediction algorithms to aid in patient treatment.

Watch Prem and Arash’s full presentation here

The Challenges

There are many challenges that are typically faced when implementing machine learning models. To add to these typical challenges, Prem and Arash were faced with the additional hurdles of having models whose results were affecting real-time questions and problems as well as remain within the laws surrounding patient confidentiality in the medical industry.

The typical challenges that were encountered included data quality, managing different data sources, and a lack of standardization. This can be particularly difficult when integrating into a medical industry where patients are the priority while the documentation and data collection are considered secondary. Solving this problem required many years’ worth of data. It was necessary to go back to 2011 to retrieve sufficient data logs and begin to build a database that was diverse enough to begin modeling.

“There are multiple types of data. Structured data, unstructured data, clinical data, imaging and genomics data all of which needed to be accounted for.”

Finding Solutions

In order to build an automated process, a centralized interface was required. This would allow for different data sources to be aggregated, cleaned, normalized, and standardized into a format that was necessary for accurate models to be built. This also allowed for data integrity to be maintained as each individual data source never directly interacted with another, preventing contamination between data sets. This centralized interface, additionally, allowed for scalable and consistent results to be produced from several different data sources.

Solving these issues were necessary to create a data pipeline that could operate efficiently and accurately. This also allowed for consistent visuals to be generated, an essential piece for clinicians to be able to quickly and correctly identify what outcomes were being predicted.

The original purpose for integrating machine learning at Mount Sinai was twofold. Using time series, predictions were made for malnutrition reporting and predictions were made for whether patients would be discharged within the next 48 hours. These specific goals introduce the secondary unique problem to implementing these machine learning models.

These models were designed to be time sensitive so that the predictions could be used as a valuable resource by clinicians when interacting with their patients on a daily basis and in real time. The accuracy of these models was great enough that after implementation, clinicians would consult these predicted results when prioritizing patient needs.

Time series function by looking at historical data points spaced out evenly in a chronological and time sensitive manner. A successful time series algorithm will identify trends based off of repetitive and time-discrete frequencies and attempt to plot future events that mimic what it has observed. Often times after a prediction is made, the predicted values will be compared to actual values as they occur to determine the accuracy of the model.

On top of being able to create models that operated and gave results in real time Prem, Aresh, and team were able to create time series models that were able to take into account additional features that would typically not be considered when clinicians would make decisions. Creating a feedback loop between the clinicians the time series modeling, Mount Sinai was able to avoid false positive and false negative diagnoses at rates that fell below the medical industry averages. The credit can be attributed to looking at additional features that the models predicted has greater significance in outcomes and being able to adjust the algorithms based off of the clinicians first hand observations. 

This process of data collection, knowledge, and learning was the building block for implementing machine learning at Mount Sinai. The data science team was able to overcome hurdles faced from the machine learning perspective and were able to collaborate with clinicians tackle the challenges experienced from the medical field.  In an effort to continue to improve, the future at Mount Sinai looks towards moving the data pipeline to a hybrid cloud, incorporating more imaging, and publishing the results in a manner that can be more broadly used within health systems. Continual improvements and learning have proven to be successful and look to continue into the future.


Tags   •   Healthcare

Comments

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts

Recent Posts

COVID-19 Curfew and Social Distancing Enforcement using AI enabled Drones - https://youtu.be/A41BASN9TA8 Amarjot Singh, PhDFounder & CEOSkyLark Labs LLC The SARS-CoV-2(also known as COVID-19 or Novel Corona Virus) infectious outbreak has rapidly…
“Ask Me Anything” with Roboticist & Program Lead of Racing Vehicles at General Motors - Harish SkumarRoboticist & Program Lead of Racing Vehicles General Motors Ai4 recently hosted an “Ask Me Anything” session with Harish…
Bottlenecks in Supply Chains & How AI Can Help - During this panel, industry experts (showed above) discussed the impact of COVID-19 on AI on Supply Chains. We’ve included a…
How COVID-19 is Impacting the State of AI in Supply Chains - During this panel, industry experts (showed above) discussed the impact of COVID-19 on AI on Supply Chains. We’ve included a…
How COVID-19 is Impacting the State of AI in Investment Management - On this panel, industry experts (listed above) discussed the impact of COVID-19 on AI on Investment Management. We've included a…
The State of AI in Investment Management - On this panel, industry experts (listed above) discussed the affects of AI on Investment Management. We've included a short transcription…
The State of AI in Banking - On this panel, industry experts (listed above) discussed what they are most excited about in AI in Banking. We've included…
How COVID-19 is Impacting the State of AI in Banking - On this panel, industry experts (listed above) discussed The State of AI in Banking and how COVID-19 is affecting it.…
The Ethics of AI: Who will be Responsible for the Decisions of AI Applications? - Ayodele Odubela Data Scientist SambaSafety One of the issues often debated in AI as it regards to ethics is who…
“Ask Me Anything” with Krzysztof Geras, PhD - Krzysztof Geras Assistant Professor NYU Department of Radiology Ai4's recently hosted an "Ask Me Anything" session with one of our…

Popular Posts

Does Healthcare AI Meet Basic Ethics Principles? - Ingrid Vasiliu-Feltes Chief Quality and Innovation Officer MEDNAX, Health Solutions Partner Over the past decade we have noticed an exponential…
AI/ML in Investment and Risk Management: Recent Applications, Use Cases, and Implementation Challenges - Arvind Rajan Managing Director - Head of Global & Macro PGIM Fixed Income Introduction Investing is a completely different ballgame…
Top AI Conferences - Interested in learning the latest in AI this year? We’ve compiled a list of the top artificial intelligence conferences in…
Artificial Intelligence & Cybersecurity: Math Not Magic - Wayne Chung CTO FBI Introduction The field of cybersecurity has slowly progressed from an art to a science. It has…
Machine Learning for Pricing and Inventory Optimization @ Macy’s - Jolene Mork Senior Data Scientist Macy's Iain Stitt Data Scientist Macy's Bhagyesh Phanse VP, Data Science Macy's Overview In this…
“Ask Me Anything” with Zappos’s Head of AI/ML Research & Platforms, Ameen Kazerouni - Ameen Kazerouni Head of AI/ML Research & Platforms Zappos Family of Companies Ai4 recently hosted an "Ask Me Anything" session…
Advancements at Siemens Healthineers in AI for Medical Imaging - Bimba Rao Head of Global Artificial Intelligence Engineering Siemens Healthineers Ultrasound Siemens Healthineers background  Siemens Healthineers builds healthcare products and…
Leveraging AI in Cybersecurity Risk Modeling & Mitigation - Christopher Novak Director, Threat Advisory Research Verizon Wireless Introduction Originally, there was a poor understanding of why cyber breaches were…
An Ensemble Approach to Predict Default Risk in Stress Testing - Yun Zheng VP of Innovation & Global Risk Analytics HSBC Overview This presentation discussed the importance of performing stress tests…
Machine Learning and Artificial Intelligence in Banking - Artit "Art" Wangperawong Distinguished Engineer US Bank Introduction Every company’s AI journey is different. We’re all trying to figure out…