Agenda

Predictive Analytics World for Healthcare Las Vegas 2019

June 16-20, 2019 – Caesars Palace, Las Vegas



This page shows the agenda for PAW Healthcare. Click here to view the full 7-track agenda for the five co-located conferences at Mega-PAW (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).

Session Levels:

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

Pre-Conference Workshops - Sunday, June 16th, 2019

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description. 

Leader
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
2:00 pm
Pre-Conference Training Workshop

Two and a half hour afternoon workshop:

This 2.5 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis. Click workshop title above for the fully detailed description.

Leader
Brennan LodgeGoldman Sachs
Data Scientist VP
Goldman Sachs
4:30 pm
End of Pre-Conference Training Workshops
CloseSelected Tags:

Pre-Conference Workshops - Monday, June 17th, 2019

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm:

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description.

Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm:

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages. Click workshop title above for the fully detailed description.

Leader
Brennan LodgeGoldman Sachs
Data Scientist VP
Goldman Sachs
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm:

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.

Leader
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Pre-Conference Training Workshop

Full-day: 8:30am – 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. Click workshop title above for the fully detailed description.

Leader
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:30 pm
End of Pre-Conference Training Workshops
CloseSelected Tags:

Day 1 - Tuesday, June 18th, 2019

8:00 am
Registration & Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
MEGA-PAW SUPER-PLENARY KEYNOTE

A veteran applying deep learning at the likes of Apple, Samsung, Bosch, GE, and Stanford, Mohammad Shokoohi-Yekta kicks off Mega-PAW 2019 by addressing these Big Questions about deep learning and where it's headed:

  • Late-breaking developments applying deep learning in retail, financial services, healthcare, IoT, and autonomous and semi-autonomous vehicles
  • Why time series data is The New Big Data and how deep learning leverages this booming, fundamental source of data
  • What's coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated
Session description
Speaker
Mohammad Shokoohi-YektaApple
Senior Data Scientist
Apple
9:15 am
MEGA-PAW SUPER-PLENARY KEYNOTE

In the United States, between 1500 and 3000 infants and children die due to abuse and neglect each year. Children age 0-3 years are at the greatest risk. The children who survive abuse, neglect and chronic adversity in early childhood often suffer a lifetime of well-documented physical, mental, educational, and social health problems. The cost of child maltreatment to American society is estimated at $124 - 585 billion annually.

A distinctive characteristic of the infants and young children most vulnerable to maltreatment is their lack of visibility to the professionals. Indeed, approximately half of infants and children who die from child maltreatment are not known to child protection agencies before their deaths occur.

Early detection and intervention may reduce the severity and frequency of outcomes associated with child maltreatment, including death.

In this talk, Dr. Daley will discuss the work of the nonprofit, Predict-Align-Prevent, which implements geospatial machine learning to predict the location of child maltreatment events, strategic planning to optimize the spatial allocation of prevention resources, and longitudinal measurements of population health and safety metrics to determine the effectiveness of prevention programming.  Her goal is to discover the combination of prevention services, supports, and infrastructure that reliably prevents child abuse and neglect.  

Session description
Speaker
Dyann Daley MDPredict Align Prevent
Founder and CEO
Predict-Align-Prevent
9:40 am
Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
dotData
10:00 am
Exhibits & Morning Coffee Break
10:30 am
Case Study: Zuckerburg San Francisco General Hospital

Bringing the benefits from AI efforts to the frontline workers continues to be a struggle across major healthcare organizations.  We worked on a novel, practical approach to directly take on the workflows of healthcare workers.  This session shares the successes and failures in our attempts, and the AI's introduced via this approach to achieve efficiency and/or outcome goals. This practical workflow approach uses AI as tools, hence can deploy various AI’s for a variety of problems including patient status tracking and task automation. AI's being directly in the workflow also enables continuous learning, process improvement, and optimization toward specific goals.

Session description
Speaker
Cheong AngUniversity of California, San Francisco
Workflow AI Project Lead
University of California, San Francisco
11:15 am
5-minute transition between sessions
11:20 am
Case Study: Predicting Cardiovascular Disease

Until recently, healthcare has not understood root causes of diseases well enough for prevention; the main approach has historically been to treat patients after onset. While primary prediction scoring systems are routine for CVD patients, the goal is to reach patients before primary events occur. Amgen and a startup partner are co-developing a machine learning solution that uses existing EMR data to develop statistical and machine learning models predicting secondary CVD events. Having more accurate risk prediction models could significantly impact approaches to disease prevention. Session will also cover role of partnership in sourcing, prototyping, piloting, and scaling novel technologies.

Session description
Speaker
Erich Wohlhieter Ph.DAmgen
Executive Director, Digital Health & Innovation
Amgen
12:05 pm
Lunch
1:30 pm
KEYNOTE

With major new players (Amazon, JP Morgan, Berkshire Hathaway), reconfigured players (CVS merged with Aetna), and lots of hospital consolidation, healthcare is going to change. We are on the cusp of a post-hospital era where advanced analytics will enable and support pay for performance, value-based purchasing, pricing optimization, wellness/disease management, evidence-based medicine, and workforce optimization. Meanwhile the government retools its metrics every few years and tries to keep up.  

This keynote will confront the role of health analytics as a major force in the changing health care landscape.  Professor Rossiter will explain how we are finally entering the post-hospital era, and how all of this will enable the long-awaited managed competition approach to health services delivery.

Session description
Speaker
Louis F Rossiter Ph.DWilliam & Mary School of Business
Professor of Public Policy at the College of William & Mary, former Secretary of Health & Human Resources for the Commonwealth of Virginia, former Deputy for Policy to the Administrator of the Center
College of William & Mary
2:15 pm
Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
diwo
2:35 pm
5-minute transition between sessions
2:40 pm
Case Study: Breaking Down the Models

A frequent criticism of the use of machine learning models as compared to human analysis is that ML models are "black boxes" and uninterpretable. Recent advancements in the field of explainable AI allow us to understand what factors influenced both individual predictions and aggregate model behaviors. We will revisit a case study from another PAW conference on predicting hospital readmissions, except this time we will use open-source software and dive into the 'why' with various visualizations that explain the model's behavior.

Session description
Speaker
Cal ZemelmanCVP
Director, Lead Data Scientist
CVP
3:25 pm
Exhibits & Afternoon Break
3:55 pm

Emergency departments have seen a dramatic increase in the number of visits from elderly patients. Many elderly use a personal emergency response system (PERS) to signal for help in case of an incident such as a fall or breathing problems. At Partners Healthcare, we are testing a predictive model that uses PERS data to predict elderly at high risk of emergency department visits. Clinical staff from our homecare program perform interventions with high-risk patients. This presentation will cover the development of the predictive model and its deployment in a randomized controlled trial.

Session description
Speakers
Sara Golas
Senior Data Specialist
Partners HealthCare Pivot Labs
Jorn op den BuijsPhilips Research
Senior Scientist
Philips Research
4:40 pm
5-minute transition between sessions
4:45 pm
Case Study: AI for Anomaly Detection

An emerging biotech company launched the first treatment option in an unestablished rare disease market. Extremely low prevalence, lack of physician awareness, no codified ICD-10 diagnosis code, and the lack of approved treatments resulted in significant mis-diagnosis, making the application of AI challenging. Addressing the challenge required combining first and third party de-identified data in a HIPAA-compliant workflow based on Swoop's prIvacy platform. Of the 84 start forms in the past 6 months, 24 (29%) were due to Swoop's AI model. Further validation is underway in a university hospital system, embedding predictions into clinical workflows to improve patient outcomes.

Session description
Speakers
Dan FisherSwoop
VP Life Sciences
Swoop
Steve LambIPM.ai
Principal
IPM.ai
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Day 2 - Wednesday, June 19th, 2019

8:00 am
Registration & Networking Breakfast
8:45 am
8:55 am
KEYNOTE

Data underlies all of our best efforts to evolve health care practices. Data, and lots of it, now come in many forms and from many sources. Data is the catalyst for the transition from volume-based, episodic care to value-based, personalized care. A workable data strategy has to account for a variety of data forms and sources. A good data strategy bakes in empathy for each individual represented by the data. And, a great data strategy ensures that any movement of data within the organization is reliable, timely, and makes provision for increased data asset value.  Great data strategy is the foundation for improving the delivery and outcomes of our healthcare experience. Gerhard Pilcher will share insights, tips, and lessons learned from more than 20 years of work solving problems and providing guidance to many different types of complex organizations within the health care industry and beyond.

Session description
Speaker
Gerhard PilcherElder Research
President & CEO
Elder Research
9:40 am
Sponsored Session
The Session Description will be available shortly.
Session description
Sponsored by
DataRobot
Speaker
Nathan Patrick Taylor
Data Scientist
DataRobot
10:05 am
Case Study: Healthcare Applications of Natural Language Processing

Dr. Talby will review case studies from real-world projects that built AI systems that use 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 machine learning and deep learning pipelines.

Session description
Speaker
David Talby Ph.D
Chief Technology Officer
Pacific AI
10:50 am
Exhibits & Morning Coffee Break
11:20 am

Healthcare has always used statistical analysis and analytic capabilities for accounting, reimbursement, actuarial and fiscal projection purposes. New developments in advanced statistical and predictive analytics techniques promise to revolutionize health and medical outcomes, and care delivery. These new techniques utilize modern machine learning and Artificial Intelligence methods to predict and prescribe at the individual level, instead of using traditional statistics. Learn how new machine learning techniques are being used for value-based purchasing, population health, healthcare consumerism and precision medicine. Peer into the future of Healthcare Data Science with predictions from industry leaders.

Session description
Speaker
Ken Yale, JD, DDSUCI, Irvine
Instructor
University of California - Irvine
12:05 pm
Lunch
1:10 pm
Expert Panel

Multiple studies and surveys reveal that the health care industry lags behind other major industries when it comes to the adoption of analytics. Questions for debate include whether or not this is a fair assessment; and, if so; why this is the case. Join our panel of experts as they explore the state of analytics in health care and discuss the obstacles and the opportunities for advancement with this important technology.

Session description
Moderator
Jeff DealElder Research
Chief Operating Officer
Elder Research
Panelists
Gerhard PilcherElder Research
President & CEO
Elder Research
Louis F Rossiter Ph.DWilliam & Mary School of Business
Professor of Public Policy at the College of William & Mary, former Secretary of Health & Human Resources for the Commonwealth of Virginia, former Deputy for Policy to the Administrator of the Center
College of William & Mary
Ken Yale, JD, DDSUCI, Irvine
Instructor
University of California - Irvine
2:00 pm
Sponsored Session
The Session Description will be available shortly.
Session description
2:10 pm
5-minute transition between sessions
2:15 pm
Case Study: Answering One of the Big Questions

How much data is enough to build an accurate model?  This is often one of the first and most difficult questions to answer early in any machine learning project.  However, the quality and applicability of your data are more important considerations than quantity alone.  This talk presents some insights and lessons learned for gauging the suitability of electronic health record (EHR) training data for a desired project.  You will see how to determine if more data might increase accuracy and how to identify any weaknesses a model might have as a result of your current training data.

Session description
Speaker
Jeff HeatonRGA
VP, Data Scientist
Reinsurance Group of America
3:00 pm
Exhibits & Afternoon Break
3:30 pm
3:30 pm - 3:50 pm

With the advent of big data and machine learning, there is an opportunity to combat rising healthcare costs by leveraging data in an ethical and privacy compliant way to establish more consistency and implantation of preventative care. We need to ensure there is a fundamental set of rules and responsibilities in place among healthcare organizations to protect their patient's privacy. In this presentation we will address this challenge and speak to the importance of creating an ethical and privacy compliant approach to aggregating multiple data sources which then can be used to improve patient outcomes.

Session description
Speakers
Casey GentryAcxiom
Senior Manager Engineering/Analytics
Acxiom
Jamie NethertonAcxiom
Senior Analyst
Acxiom
3:55 pm - 4:15 pm
The Session Description will be available shortly.
Session description
4:15 pm
5-minute transition between sessions
4:20 pm

Independent Pediatricians typically maintain daily patient volumes of 20-30 patients to keep their practices viable. Pediatricians also schedule appointments up to a year in advance, leading to as many as 15% of patients not showing up for appointments each day. The financial and clinical impact of these gaps in pediatric appointment books is substantial.

PCC and Rexer Analytics analyzed pediatric no-show patterns to identify the variables that truly affect appointment truancy. These insights were translated into interventions to reduce patient truancy. We present pediatric no-show patterns, key predictors, and the results several Pediatric practices are seeing with targeted interventions.

Session description
Speakers
Chip Hart
Director of Pediatric Solutions
Physician’s Computer Company
Karl RexerRexer Analytics
President
Rexer Analytics
5:05 pm
End of second Conference Day
CloseSelected Tags:

Post-Conference Workshops - Thursday, June 20th, 2019

8:30 am
Post-Conference Training Workshop

Full-day: 8:30am – 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. Click workshop title above for the fully detailed description.

Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm:

Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.

Leader
James Casaletto
Senior Solutions Architect
MapR Technologies
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm:

During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) – held the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences. Click workshop title above for the fully detailed description.

Leader
Martin GornerGoogle
Developer Relations
Google
4:30 pm
End of Post-Conference Training Workshops
CloseSelected Tags:
Share This

Get Predictive Analytics World news and event information delivered straight to your inbox.