Agenda

Predictive Analytics World for Healthcare 2022

June 19-24, 2022 l Caesars Palace, Las Vegas


To view the full 7-track agenda for the six co-located conferences at Machine Learning Week click here or for the individual conference agendas here: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate or Deep Learning World.

All times are Pacific Daylight Time (PDT/UTC-7)

Workshops - Sunday, June 19th, 2022

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm. 

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general.

 

Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
4:30 pm
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Workshops - Monday, June 20th, 2022

8:30 am
Pre-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.

The Workshop Description will be available shortly.
Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Pre-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning.

The Workshop Description will be available shortly.
Instructors
Leo BetthauserMicrosoft
Senior Data Scientist
Microsoft
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:30 pm
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Predictive Analytics World for Healthcare - Las Vegas - Day 1 - Tuesday, June 21st, 2022

8:00 am
Registration & Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am

Nvidia's Siddha Ganju has gained a unique perspective on machine learning's cross-sector deployment. In her current role, she work's on a range of applications, from self-driving vehicles to healthcare, and  she previously led NASA's Long-Period Comets team, applying ML to develop meteor detectors. Deep learning impacts the masses, so it demands mass, interdisciplinary collaboration. In this keynote session, Siddha will describe the very particular interdisciplinary effort -- driven by established joint directives -- required to successfully deploy deep learning across a variety of domains, including climate, planetary defense, healthcare, and self-driving cars.The format of this session will be a "fireside chat," with PAW Founder Eric Siegel interviewing Siddha in order to dig deep into the lessons she's learned.

Session description
Speaker
Siddha GanjuNvidia
Sr. Data Scientist, Self-Driving Vehicles, Medical Instruments & Deep Learning
NVIDIA
9:15 am

Machine learning and robotics are dramatically shifting our industrial capabilities and are opening new doors to our functional understanding and ways to support the natural world. Together, these advances can enable something far beyond simply limiting our damage to the planet -- they create the possibility of building a new relationship to nature wherein our industrial footprint can be radically reduced and nature's capability to support itself and all life on Earth (including us!) can be amplified.

Session description
Speaker
Tom ChiAt One Ventures
Former cofounder of Google X & Founder
At One Ventures
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee
10:30 am

Blood, platelets and other transferable fluids are critical for patient health.  At PAW-2020 we described OneBlood’s use of analytics to optimize blood donor recruitment, to forecast hospital needs and to manage the blood supply chain during the Covid pandemic.  Now we provide an update with a focus on platelets.  We built and deployed three predictive models.  Marketing campaigns use these models, and dashboards enable campaign tracking and iterative improvements.  Inventory monitoring and hospital demand forecasting are the remaining solution components.  With all of these components working together, we have dramatically increased the number of platelet donors, stabilized inventory to match demand, and dramatically increased platelet availability in Florida hospitals.

Session description
Speakers
Kelley CountsOneBlood
Director of Data Science
OneBlood
Karl RexerRexer Analytics
President
Rexer Analytics
11:15 am
Short Break
11:25 am

In January 2020, just after the first case of COVID-19 was discovered in the US, NYU Computer Science Prof. Anasse Bari led with infectious disease medical doctor Prof. Megan Coffee a multidisciplinary team of AI and medicine experts both in the US and China to develop the first COVID-19 Clinical Severity Predictive Tool. The tool aimed to help medical doctors triage and provide care effectively during the incoming surges of cases by using algorithms that can predict which mildly ill patients were likely to become severely ill. In July 2021, the team developed another tool named COVID-19 Early-alerts Signals built on a digital epidemiology framework that analyzes alternative data sources to discover predictors of the pandemic curve, which could supplement traditional predictive models and inform early warning systems and public health policies. The research finds that online google searches can predict major regional increases and decreases in COVID-19 cases.  After the vaccine rollout, Prof. Bari and Coffee led a team that developed a Vaccine Hesitancy Analytics Tool which is a real-time big data analytics cloud application to track misinformation and extract themes and topics related to vaccine hesitancy. The platform was based on natural language processing and sentiment analysis predictive algorithms. The tool was deployed using Amazon Web Services. 

 
In this talk Prof. Bari will outline the experimental research results from the three tools his team developed: (1) COVID-19 Clinical Severity Predictor, (2) Pandemics Early-alert Signals Tool based on alternative data, and (3) Vaccine Hesitancy Analytics Tool. This talk will also highlight the analytics lessons learned and how we can better prepare for future pandemics using predictive analytics and algorithms.
 
* Prof. Anasse Bari led these projects and teams with medical doctor Prof. Megan Coffee, Dr. Matthias Heymann and other researchers from the NYU Courant Institute of Mathematical Sciences, the NYU Computer Science Department and the NYU Grossman School of Medicine

Session description
Speaker
Anasse Bari Ph.D.New York University
Professor of Computer Science - Director of the AI and Predictive Analytics Lab
New York University
12:10 pm
Lunch
1:30 pm

New advances in natural language processing have recently started moving from research to real-world production implementations. The session reviews recent case studies in several of the USA's largest healthcare systems and pharmaceuticals that applied novel research in deep learning and transfer learning to better answer medical questions, enable real-world data, predict patient outcomes and population risk, and anonymize data at scale. This session is intended for people looking to understand what it possible right now - and what are the lessons learned from the early adopters.

Session description
Speaker
David Talby Ph.DJohn Snow Labs
Chief Technology Officer
John Snow Labs
2:15 pm
The Session Description will be available shortly.
Session description
2:45 pm

Building and deploying predictive models for the COVID-19 pandemic was challenging and most of the models have not performed as well as hoped.  I cover five lessons learned from analyzing data by the Pandemic Response Commons, a not-for-profit that collects, analyzes and shares COVID-19 related data in the Chicago region.  I also look at the challenges of understanding COVID-19 health disparities and present the results of models showing the unequal impact of COVID-19 on different populations.  We conclude by discussing how regions can prepare for the future by putting in place persistent infrastructure for regional data collection, analysis and sharing.

Session description
Speaker
Robert Grossman
Frederick H. Rawson Professor of Medicine and Computer Science
The University of Chicago
3:30 pm
Exhibits & Afternoon Break
4:00 pm

Efficiently and reliably producing value through data science requires the development of industry leading solutions, strong operational partnerships and standardized processes.  OSF HealthCare has been successfully building and deploying data science products internally for more than nine years.  In this talk, Nick Wiechman, OSF's Manager of Data Science Services will use recent case studies to walk the audience through how OSF's functional structures allow the team to rapidly product reliable value in an agile aligned manner.

Session description
Speaker
Nick WiechmanOSF Healthcare
Manager of Data Science Services
OSF Healthcare System
4:45 pm
Short Break
4:55 pm

Robust estimation of when, or if, a patient will refill a prescription requires accounting for intrinsic patient-level factors and environmental conditions, plus specific interventions focused on adherence. Bayesian multi-factor models provide a general means of incorporating data across all scales, accounting for the group-wise variation of arbitrary combinations of factors. Fitting robust and reliable Bayesian models, however, is difficult in practice, and much has been done recently to describe a "Bayesian workflow." We will demonstrate one approach to developing a Bayesianmulti-factor model using best-practice workflows to regularize our exploration and provide a concise and expressive model.

Session description
Speaker
Tom Shafer PhDElder Research
Lead Data Scientist
Elder Research
5:40 pm
Networking Reception
7:00 pm
End of Conference Day 1

Predictive Analytics World for Healthcare - Las Vegas - Day 2 - Wednesday, June 22nd, 2022

8:00 am
Registration and Networking Breakfast
8:45 am
Chris FranciskovichOSF Healthcare
Director, Advanced Analytics
OSF Healthcare System
8:55 am

In order for predictive analytics to have the most impact possible on patient care, we need to be able to focus on the specific population that is being treated. However, to prevent every organization from solving the same problem, we also need to create predictive analytics that generalize well. This tension drives many decisions in the model development process, from how we gather and analyze data, to how we support healthcare organizations that are deploying predictive models. We will discuss our approach to developing and deploying machine learning solutions with these two goals in mind.

Session description
Speaker
Owen SizemoreEpic
Director of Machine Learning for Revenue & Access
Epic systems
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
Short Break
10:10 am

Long-term health consequences of COVID-19 are symptoms that continue weeks or months after first diagnoses. Symptoms span respiratory, neurological, psychological, and cardiac problems and range from mild to debilitating.  Little is known about the risk factors contributing to long COVID, whether vaccines play a role or the best treatment options. UnitedHealth Group data represents millions of COVID-19 patients – some fully recovered and others that suffer from continued health consequences. Machine learning provides the opportunity to characterize these risk factors and predict probability that future disease will occur.

Session description
Speaker
Danita KiserOptum UnitedHealth Group
Vice President of Research Collaborations
Optum Technology
10:55 am
Exhibits & Morning Coffee Break
11:25 am

A key to meaningfully and sustainably accelerating patient  flow, improving quality, and saving caregiver time, is having the ability to spot situations and risks early, so caregivers and expediters can intervene in the moment.  Real-time, contextual information that is simple to digest and easy to access, is the currency with which to make this happen.  By combining clinical expertise, real-time and predictive analytics, and pre-defined action sets, Humber River Hospital in Toronto, Canada, is unlocking capacity, improving protocol compliance and reducing patients sent to ICU, while at the same time improving care team communication and reducing caregiver stress.

Session description
Speakers
Peter BakHumber River Hospital
CIO
Humber River Hospital
Zahava UddinGE Healthcare
Managing Director
GE Healthcare
12:10 pm
Lunch
1:15 pm
The Keynote Description will be available shortly.
Session description
2:00 pm
The Session Description will be available shortly.
Session description
2:15 pm

Speakers: Mariana Nikolova-Simons, Rikkert Kelderman


Session description
3:00 pm
Exhibits & Afternoon Break
3:30 pm

We explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared predictive accuracy of the models based on PM datasets from a single but more accurate air monitoring station in each district and multiple but less accurate monitoring sites within a district in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.

Session description
Speakers
Dohyeong Kim Ph.D.
Professor of Public Policy, GIS and Social Data Analytics and Research
University of Texas at Dallas
Sung-Chul Seo
Professor of Nano, Chemical & Biological Engineering
Seokyeong University
4:15 pm
Short Break
4:25 pm

The ultrasound examination is one of the most common techniques of medical imaging. FOLLISCAN is an analytical and predictive system based on interpretable deep learning algorithms to support healthcare practitioners in ultrasound ovaries diagnostics - antral follicles examination. The counting of follicles is of major importance, e.g., it helps estimate the ovarian reserve. Moreover, a large number of antral follicles indicates polycystic ovarian morphology.

FOLLISCAN is designed for fertility clinics, diagnostic centers and, hospitals and will respond to their well-identified need, i.e. time and cost savings - through wider access to ultrasound diagnostics to make better and more informed clinical decisions. The results of the project address the need to perform ultrasound examinations in a faster, easier, more objective and accurate manner. 
In this talk, we will describe the process of building this solution and show the most important issues for creating a solution based on deep learning.
FOLLISCAN is currently tested and implemented in all INVICTA fertility clinics across Poland.

Session description
Speaker
Piotr Wygocki Ph.D.MIM Solutions
CEO & Co-Founder at MIM Solutions Assistant Professor at University of Warsaw
MIM Solutions
5:10 pm
End of Conference Day 2
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Workshops - Thursday, June 23rd, 2022

8:30 am
Post-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

This one day workshop reviews major big data success stories that have transformed businesses and created new markets.

The Workshop Description will be available shortly.
Instructors
Vladimir BarashGraphika Labs
Chief Scientist
Graphika
Marc SmithConnected Action Consulting Group
Chief Social Scientist
Connected Action Consulting Group
Post-Conference Training Workshop

Full-Day 8:30 am - 4:30pm  

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.

The Workshop Description will be available shortly.
Instructor
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
5:30 pm
Post-Conference Training Workshop

3 hour workshop: 5:30-8:30pm

This 3 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis.

The Workshop Description will be available shortly.
Instructor
Jared LanderLander Analytics
Chief Data Scientist
Lander Analytics
8:30 pm
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Workshops - Friday, June 24th, 2022

8:30 am
Post-Conference Training Workshop

Full-Day 8:30 am - 4:30pm 

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.

The Workshop Description will be available shortly.
Instructor
Jared LanderLander Analytics
Chief Data Scientist
Lander Analytics
4:30 pm
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All times are Pacific Daylight Time (PDT/UTC-7)