Las Vegas 2020 Agenda will be released shortly.
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).
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Pre-Conference Workshops - Sunday, June 16th, 2019
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.
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.
Pre-Conference Workshops - Monday, June 17th, 2019
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.
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.
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.
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.
Day 1 - Tuesday, June 18th, 2019
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
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.
The research on the state of Big Data and Data Science can be truly alarming. According to a 2019 NewVantage survey, 77% of businesses report that "business adoption” of big data and AI initiatives are a challenge. A 2019 Gartner report showed that 80% of AI projects will “remain alchemy, run by wizards” through 2020. Gartner also said in 2018 that nearly 85% of big data projects fail. With all these reports of failure, how can a business truly gain insights from big data? How can you ensure your investment in data science and predictive analytics will yield a return? Join Dr. Ryohei Fujimaki, CEO and Founder of data science automation leader dotData, to see how Automation is set to change the world of data science and big data. In this keynote session, Dr. Fujimaki will discuss the impact of Artificial Intelligence and Machine Learning on the field of data science automation. Learn about the four pillars of data science automation: Acceleration, Democratization, Augmentation and Operationalization, and how you can leverage these to create impactful data science projects that yield results for your business units and provide measurable value from your data science investment.
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 AIâ€™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.
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.
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.
The need for timely decisions in ever-changing, unique scenarios is carved out in the healthcare field, where leveraging AI is expected to become a $6.6 Billion market. diwo's Cognitive Framework provide pre-packaged, quantified decisions for healthcare, turning insights into the best possible action. Will include a demonstration of diwo’s cognitive decision making for Readmission.
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.
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.
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.
Day 2 - Wednesday, June 19th, 2019
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.
Machine learning techniques in healthcare often get the bad reputation of being Black box methods. This session will bust that myth to show how interpretability tools can give more confidence in a machine learning model, but also help to improve the insights you generate from it. This talk will cover best practices for using techniques such as feature importance, partial dependence, and explanation approaches. Along the way, we will consider different issues that may affect model interpretation and performance.
The speaker 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.
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.
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.
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.
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.
Advanced analytics transforms data into actionable insights, augmenting human decision making in manners previously impossible. At OSF Healthcare, properly formulated and deployed advanced analytics solutions allow us to save and improve more lives, decrease the cognitive load for our mission partners, improve our financial performance and generating transformative innovations.
This session will share some of the best examples of beneficial advanced analytics solutions over OSF Healthcare’s 5+ year journey in the space. Highlights will include clinical improvements achieved through the integration of risk modeling and decision support tools, operational efficiencies empowered by risk assessment automation and clinical information extraction through natural language processing.
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.
Post-Conference Workshops - Thursday, June 20th, 2019
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.
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.
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.