Agenda

Predictive Analytics World for Healthcare 2021

May 24-28, 2021


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.

Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Orange square sessions are Practitioner Level

THIS IS LAST YEAR’S AGENDA — THE 2022 AGENDA WILL BE POSTED HERE SOON. All times are Pacific Daylight Time (PDT/UTC-7)

Workshops - Wednesday, May 19th, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm PDT

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
7:30 am
Workshop:

Full-day: 7:30am – 3:30pm PDT

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
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee
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Workshops - Thursday, May 20th, 2021

8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

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

The Workshop Description will be available shortly.
Instructor
Dean AbbottWunderkind
Chief Data Scientist
Wunderkind
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

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.

The Workshop Description will be available shortly.
Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

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
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Workshops - Friday, May 21st, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm PDT

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
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Monday, May 24th, 2021

8:45 am
9:00 am
Lessons from: Amazon

Amazon's vision is to be earth's most customer-centric company. This talk explores how the Alexa Hybrid Science team in Pittsburgh, PA applies a customer-centric lens to cutting-edge machine learning research. The team is responsible for developing on-device Alexa automatic speech recognition models to provide a faster, more reliable Alexa experience. Our research includes neural network compression techniques, end-to-end spoken language understanding and optimizing machine learning for edge devices.

Session description
Speaker
Nathan SusanjAmazon.com
Applied Science Manager
Amazon
9:50 am

The quality of any machine learning or deep learning model depends on the values that define the model structure and corresponding hyperparameters. Many practitioners may find themselves investing countless hours manually searching for the right model and related hyperparameter values. Some use highly inefficient grid search methods. Others will use simple random sampling, which actually works fairly well. But alone, this method only offers a globalized search, and other sampling methods may be better suited to the job. 

Why not use machine learning to automate the search for the best model? 

This presentation details an advanced approach that uses both global and local search strategies that can be evaluated in parallel to ensure a quick and efficient exploration of the decision space. In the case of this presentation, a genetic algorithm (GA) will be examined for the global search because the selection and crossover aspects of a GA distinguish it from a purely random search. A generating set search (GSS) will be used to greedily search the local decision space. 

Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
10:10 am
Room change
10:20 am
Case Study: Precision Medicine

A single doctor, overworked and overwhelmed, will make a less-informed decision than a team of doctors. Now, imagine the potential knowledge we could gain if we combined insight from doctors all over the world. In this case study, we will explain how Innovative Precision Health (IPH) uses large amounts of aggregated anonymous population data to learn about the trajectory of a disease and predict the most effective treatment for a patient.  By exponentially increasing the amount of information available, IPH is able to provide better predictions for doctors, better care for patients, and profitable outcomes for insurance and pharmaceutical companies alike.

Session description
Speaker
Mark GudesblattInnovative Precision Health
Chief Medical Officer
Innovative Precision Health
11:05 am
Break & Expo Hall
11:30 am
Improving Health with Analytics

This session details for big data collected on over 1.4 million people has been used to develop AI-led, personalised interventions that are clinically-proven to reverse type 2 diabetes, which has redefined the use of digital therapeutics in the USA, Canada, United Kingdom, Germany and India. 

Session description
Speaker
Arjun PanesarDiabetes Digital Media (DDM)
Founder and CEO
Diabetes Digital Media
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 1
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Tuesday, May 25th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Jeff DealElder Research
Chief Operating Officer
Elder Research
9:00 am
TUESDAY KEYNOTE

Medicine is an ever-unfolding quest to ensure patients receive life improving therapies to return to full life. Advances in intelligent data, AI, data automation, machine learning, and computational capability allow an efficient pursuit of better outcomes for patients while reducing health care costs. To help understand new approaches to healthcare and new forms of innovation, Maneesh Shrivastav PhD, Director of Market Development, Science and Analytics at Medtronic, will run through innovation at the medical technology company and provide examples of how the company is leveraging data science to improve patients’ lives.

Session description
Speaker
Maneesh Shrivastav Ph.D.Medtronic
Director of Market Development
Medtronic
9:50 am

Constructing predictive models using healthcare claims data often requires complex feature engineering and extensive clinical domain knowledge, a tedious, time-consuming and error-prone process. To address this challenge and enable rapid model production, Geneia data scientists developed an automated pipeline to construct machine learning models with little or no manual intervention. 

In the automated pipeline, the diagnosis and medication data in raw claims were aggregated into clinically meaningful ‘groupers’, and then the ‘groupers’ were fit into the pipeline for automated model construction. Using this approach, Geneia data scientists were able to speed up the model construction process by around 100 times, while maintaining high accuracy and good interpretability. 

Geneia data scientist Zhipeng Liu will discuss the creation of the automated pipeline and an application, a series of models for predicting the onset of major chronic diseases. 

Session description
Sponsored by
Geneia
Speaker
Zhipeng LiuGeneia
Principal Data Scientist
Geneia LLC
10:10 am
Room change
10:20 am
Turning Data into Action

Anyone can make a pretty bar graph, but can you make sound decisions based on that graph? Is it actionable, or is it only fluff? How do you turn flashy concepts into actionable visualizations? Can you see the end result of those concepts; will they ever become reality? Do you have the vision to combine beauty with brains, thereby driving decisions with data? Or do you settle for destroying direction with disaster? American mathematician John Tukey once said, "The greatest value of a picture is when it forces us to notice what we never expected to see." What value do you see in your data? And what ideas do you have when you see it? Learn how you can capitalize on your ideas by blending internal with external, leveraging them into a cohesive strategy for both the short term AND the long term. See the five "Stages of the Spectrum" in action while discovering the difference between impact and influence, and how that difference plays into making data actionable. Catch the right blend of art and science, or beauty and brains, as you go from concept to reality. 

Session description
Speaker
Joe Perez Dr.NC Dept of Health & Human Services
Senior Systems Analyst / Chief Technology Officer
NC Dept of Health & Human Services / SolonTek Corporation
11:05 am
Break & Expo Hall
11:30 am
Case Study: Improving Emergency Room Care and Efficiency

Hospitals make lots of efforts to improve their capacity in emergency departments by adding boxes and beds, organizing shifts, processes and protocols. And, by also implementing systems to record, control and visualize better what's the situation in real time. But decisions, the ones that ultimately drive the output of the process, are entirely left to human capacity. What if an algorithm could help selecting the sequence for attending emergency patients so that life threatening situations are prioritized and overall queueing time is shortened, with no change in the physical resources? Benjamin Arias Gálvez shares the experience at one of the largest public hospitals in Chile, where an algorithm helps sequencing the waiting queue of patients at in the emergency room in real time, with no investment in the physical resources.    

Session description
Speaker
Benjamin Arias-GálvezForesta.io
General Manager
Foresta.io
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 2
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Wednesday, May 26th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Jeff DealElder Research
Chief Operating Officer
Elder Research
9:00 am
Special Plenary

Models generalize best when their complexity matches the problem.  To avoid overfit, practitioners usually trade off accuracy with complexity, measured by the count of parameters.  But this is surprisingly flawed.  For example, a parameter is equivalent to one "degree of freedom" only for regression; it can be > 4 for decision trees, and < 1 for neural networks.   Worse, a major source of complexity -- over-search — remains hidden.  The vast exploration of potential model structures leaves no trace on the final (perhaps simple-looking) model, but has outsized influence over whether it is trustworthy.

I’ll show how Generalized Degrees of Freedom (GDF, by Ye) can be used to measure the full complexity of algorithmic modeling.  This allows one to fairly compare very different models and be more confident about out-of-sample accuracy.  GDF also makes clear how seemingly complex ensemble models avoid overfit, and lastly, reveals a new type of outlier -- cases having high model influence.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
9:50 am

Deep learning models for forecasting and planning have shown significant promise for handling multiple variables, uncovering hidden patterns, and producing accurate forecasts. However, as one might expect, deep learning models are also complex and rife with pitfalls. Since these techniques often seem like a ‘black box,’ managers -- both technical and nontechnical backgrounds -- can find them hard to master.

In this session, Senior Data Scientist, Javed Ahmed will focus on the intuition behind various deep learning approaches, explore how managers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.
Attendees at the Metis session will leave with the tools to:

●      Identify types of forecasting applications that can benefit from deep learning 
●      Broadly understand deep learning approaches relevant to forecasting 
●      Understand pitfalls related to deep learning approaches, and why simpler models may work better
●      Evaluate the results of a forecasting program

Session description
Sponsored by
Metis
Speaker
Javed AhmedMetis
Senior Data Scientist
Metis
10:10 am
Room change
10:20 am
Case Study: Capital District Physicians' Health Plan

The health care industry is changing rapidly, therefore, it’s necessary to improve efficiency to production with appropriate and targeted automation. CDPHP, a mid-size payer in New York’s Capital Region, is implementing an Analytics Factory to achieve this end. It does this using a CI/CD/CT framework. In this session, you will learn about our strategy, as well as practical lessons learned on scaling and operationalizing data products. Three use cases will be discussed to illustrate the streamlined process to production: (1) NLP-driven quality measure detection, (2) prioritizing member voice responses for action, and (3) deploying and hosting a readmission model. 

Session description
Speaker
Matthew PietrzykowskiCapital District Physicians’ Health Plan
Director, Data Science & Transformational Analytics
Capital District Physicians' Health Plan
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
Case Study: Real World Application of Natural Language Processing

The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in distributed environment. 

Session description
Speaker
Veysel KocamanJohn Snow Labs
Lead Data Scientist
John Snow Labs
12:15 pm
End of Day 3
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Thursday, May 27th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Jeff DealElder Research
Chief Operating Officer
Elder Research
9:00 am
EXPERT PANEL

Analytics adoption in healthcare has come a long way over the last two decades. In the early years, advanced machine learning was limited to a handful of experts working on narrowly-defined subjects such as hospital readmissions. Among most healthcare professionals, there was limited understanding of what machine learning is, and even less eagerness to integrate analytic models into medical practice or organizational operations in any meaningful way. Today, any healthcare organization that is not using advanced analytics is an anomaly. Analytic methods are being employed almost everywhere in healthcare from insurance to precision medicine with tailored treatment plans guided by artificial intelligence. And, analytics are embedded in the systems used by hospitals, such as the HIS or the laboratory technology. Slowly, advanced analytics is being integrated throughout the healthcare system even though many professionals are not aware of its influence. Join our expert panel as we discuss the current state of analytics/machine learning, the work that remains, and opportunities to better use this incredible technology. Attendees are invited to join in the conversation and contribute questions and comments to the discussion.

Session description
Moderator
Jeff DealElder Research
Chief Operating Officer
Elder Research
Speakers
Mark GudesblattInnovative Precision Health
Chief Medical Officer
Innovative Precision Health
Sara StevensCDPHP
Vice President of Analytics Operations
CDPHP
9:50 am

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:10 am
Room change
10:20 am

OSF Healthcare has been successfully internally developing and deploying advanced analytics solutions for the past eight years.   During that time, our work has been tightly focused on a limited set of core clinical areas of our service Ministry.  Driven in large part by the success achieved through this work, OSF executive leadership made the strategic decision to expand Advanced Analytics offerings to become truly enterprise scale.  This session will showcase the structure and service offering adjustments we’ve made over the past year to support this transformation.  The speaker will also highlight specific projects empowered through these adjustment.

Session description
Speaker
Chris FranciskovichOSF Healthcare
Director, Advanced Analytics
OSF Healthcare System
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
Protecting the Pharmaceutical Supply Chain

Counterfeited, adulterated, and stolen pharmaceuticals are threats to US citizens. The Drug supply Chain Security Act (DSCSA) was established to secure the supply chain through various means including the establishment of an interoperable traceability system. The current industry stakeholder design is a distributed structure with exchanges between established trading partners. While this is flexible, it creates challenges to detecting nefarious activity and performing supply-demand management. We have implemented an interoperable, traceable prototype system which solves some of the problems and fulfills DSCSA requirements that must be met by 2023 .

Session description
Speaker
Jaya TripathiMITRE
Principal, Data Analytics
MITRE Corporation
12:15 pm
End of Day 4
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Friday, May 28th, 2021

8:55 am
Jeff DealElder Research
Chief Operating Officer
Elder Research
9:00 am

Data scientists and management have different ways of thinking and for good reason: their jobs are quite different! Moreover, data scientists, for all of their strengths, are often not the best communicators to business leaders. These differences, unfortunately, can interfere with the success of analytics projects. A particular problem for non-technical management is understanding how to help data scientists to focus on solving the business problem in the right way.

This talk will raise five questions that should be asked of data scientists. These questions are critical for setting up the problem properly and assessing the models in a manner commensurate with the business objectives.

Session description
Speaker
Dean AbbottAbbott Analytics
President
Abbott Analytics
9:55 am
The Session Description will be available shortly.
Session description
Sponsored by
Coursera
10:00 am

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:20 am
Evaluating COVID Policy

From the beginning of the global pandemic, the efforts to make COVID-19 case and death reports and other related data available to the public have been immense. This raised the hope of ambitious modelers worldwide to understand the disease and what policy measures would be effective in mitigating the pandemic. But immense problems with both the data and the modeling techniques led to false expectations, false claims, and in the end, mistrust of scientific claims generally.I will review some of the misqueues and analytic successes, demonstrating the key differences. We will see how, despite crippling weaknesses inherent in the publicly available data, sound analytical modeling techniques could reliably reveal actionable early insights for the pandemic, and coincidentally, influenza. The analytic lessons learned from this global experience can and must inform health analytics in the future.

Session description
Speaker
Mike ThurberElder Research
Principal Scientist
Elder Research
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
Case Study: Reducing Ambulance Transports

Germany is a “super-aged” society with increasing demands placed on the healthcare system due to the rise in chronic diseases. The country increasingly leverages e-health solutions to accommodate a healthier older population. In conjunction with Germany’s largest health insurer Techniker Krankenkasse, we are conducting a pilot study in which we deploy predictive modeling to identify elderly at risk of emergency ambulance transport based on e-health data. A case manager reaches out to predicted high-risk patients and recommends interventions. Initial results demonstrate a significant reduction in ambulance dispatch rate. This presentation will cover predictive model development, deployment and preliminary findings. 

Session description
Speaker
Jorn op den Buijs PhDPhilips Research
Senior Scientist
Philips Research
12:15 pm
End of Conference
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All times are Pacific Daylight Time (PDT/UTC-7)