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Agenda
Predictive Analytics World for Healthcare 2023
June 18-22, 2023 l Red Rock Casino Resort & Spa, Las Vegas
To view the full 7-track agenda for the five 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 or Deep Learning World.
Workshops - Sunday, June 18th, 2023
Full-day: 8:30am – 4:30pm PDT
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.
Workshops - Monday, June 19th, 2023
Full-day: 8:30am – 4:30pm PDT
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Full-day: 8:30am – 4:30pm PDT
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.
Full-day: 8:30am – 4:30pm PDT
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.
Predictive Analytics World for Healthcare - Las Vegas - Day 1 - Tuesday, June 20th, 2023
Machine Learning Week Founder Eric Siegel will welcome you to the event and kick things off.
Join Kian Katanforoosh, CEO and Founder of Workera, as he explores the profound impact of generative AI on the workforce and the evolution of personalized learning. With his rich experience in AI education and having taught AI to over 4 million people with Prof. Andrew Ng as founding member of DeepLearning.AI, Kian's insights are uniquely informed and forward-thinking.
In this keynote, Kian will unravel how generative AI is reshaping learning, emphasizing the pivotal role of skills data in actualizing personalized learning. He will discuss the harnessing of this data to tailor learning experiences to individual needs, track progress, identify improvement areas, and improve workforce management.
Drawing from his experiences as a founding member of DeepLearning.AI and the co-creator of the popular Stanford Deep Learning Class, Kian will share his vision for a future where learning is as unique as we are. Attend this session for a deep dive into the convergence of AI, personalized learning, and workforce transformation.
Google continues to take a bold and responsible approach to developing and deploying AI through the company’s infrastructure, tools, products, and services. Google brought further AI breakthroughs into the real world through Google Cloud’s launch of the next wave of generative AI across core areas of their business, and new partnerships and programs grounded in Google’s commitment to an open AI ecosystem. At the same time, AI, as a still-emerging technology, poses complexities and risks; and the development and use of AI must address these risks in a structured, transparent, and accountable way. A robust governance structure – and rigorous testing and ethics reviews — is necessary to put responsible AI principles into practice. And with AI regulation coming soon, Jen will share learnings, challenges, and practical tips on how Google is maturing its responsible AI practices, processes, and tools in advance of greater regulatory, global standards, and consumer expectations.
In this session, Juan Acevedo, a machine learning architect at Google, will discuss how organizations can leverage Google Cloud's generative AI products to bring value to their businesses in a secure environment and responsibly. Juan will cover the following topics: What can you do right now with Google Cloud technology Responsible generative AI This session is for those who are interested in learning more about generative AI and how it can be used to improve their businesses.

Inside healthcare, data science teams often solve each project as an independent solution. While they are able to produce significant value, project timelines can be extensive and the slow time to value impedes organization knowledge gain. In this session, I’ll show how our team has incorporated standard project structures, solution templates, supportive automations and a focus on generalized solution design to significantly decrease our time to value.
In this session, we will report on major learnings and takeaways for translating AI models into clinical practice. Mayo Clinic has created a systematic assessment of AI models using multidisciplinary subject matter expertise for projects at all phases of development (from ideation to implementation and monitoring) to assess whether AI is the right solution, advise on next steps for regulatory considerations, make recommendations for electronic health record implementation, facilitate the clinical endorsement process, etc.
Our care providers have challenges, but those challenges rarely arrive looking like data science questions. How do we use the skills of data scientists to deliver what providers really need?
Despite the rapid evolution of AI, projects still fail at a disappointingly high rate. In the past, capturing data at scale and building models was the challenge, but today we're confronted with the issue of making AI more robust while avoiding the risk of unintended consequences. While the tools are new, many challenges remain the same. In this talk, I will share by way of real-world examples improving business processes at University Hospitals, a tier 1 trauma hospital: * How to build the business case for an AI project (and get buy-in) * Navigating AI project management to prevent failure * How to mitigate the risks of unintended consequences from using A
The massive dimensionality of genomic data combined with the lack of knowledge of gene function have been major barriers to the use of Artificial Intelligence (AI) in genomics. The number of population sequencing initiatives is rapidly increasing. As these large genomic datasets grow and become more widely available, the prospect of using artificial intelligence to gain insights from this data becomes more tangible and the use of such technology becomes even more important. In this session we review the current state and future directions of using AI with genomic data.
Often data science teams, particularly in non-academic health systems where resources are scarce, will face difficulty in securing the green light for internal predictive modeling efforts. Along with technical requirements, equally important for realizing value are the strategies in presenting the business case for internal development to executive decision-makers. This case study will cover the process of advocating for data science teams in build vs. buy discussions through deploying a custom and adjustable Target Length of Stay model within clinical workflows in partnership with end users.
Many data science and predictive modeling projects get stuck between research/development and being deployed into operations. In this panel, industry experts with experience deploying production products will provide tips and insights on how to best do so in your organization.
Predictive Analytics World for Healthcare - Las Vegas - Day 2 - Wednesday, June 21st, 2023
Conference Chair Chris Franciskovich will welcome you to day 2
Assessing safety and to ensure benefits outweigh risks is essential during clinical development of medicines and vaccines. Once approved and products are used more widely, healthcare provision can evolve as can the safety profile. Timely identification and monitoring of safety outcomes across multiple different types of healthcare data from around the world are crucial elements of product lifecycle management. The volume and heterogeneity of data means that quantitative approaches, including machine learning, play a critical role in sensitive, trusted, highly regulated systems to ensure patient safety. This session will describe current advanced analytics and barriers and opportunities for advances.
Enjoy some machine learning laughs with Evan Wimpey, a predictive analytics comedian (and we're not just talking about his coding skills). No data topic is off-limits, so come enjoy some of the funniest jokes ever told at a machine learning conference.*
* Note the baseline.
This case study presents how Hospital Israelita Albert Einstein, leading Latam healthcare organization, utilized advanced analytics to determine the optimal layout for their new Cancer Center hospital. In 2021, as part of their expansion plan, they have decided to build a new Cancer Center in São Paulo, migrating their oncological operation to the new site.
The project for the new hospital was based on three pillars: Clinical Safety, Architecture and Operational Efficiency. The latter was the focus for the analytics’ team effort, using process mining, forecasting the future demand and optimization to generate the ideal layout to minimize movement flows.
The approach taken was based on 3 workstreams:
- Use of process mining to generate clinical pathways (CP) for all oncology patients in the current operation
- Clustering algorithms to classify the patients considering the different medical specialties used in their CPs
- An optimization model was developed, using mathematical models and metaheuristics, to receive the future demand for each CP along with resources’ requirements and generate the optimal layout suggestion
We captured impressive results: >15% reductions in movements for patients, visitors and materials and >8% for health staff, generating cost savings of approximately 3% of total operating cost.
This talk provides a 3-dimensional framework for understanding professional competence and career progression. The framework covers primary skills (analytics), complementary skills (oft overlooked "soft skills"), and impact (how one's work advances the mission). Once defined, the speaker provides actionable guidance on how to develop a roadmap to achieve raises, bonuses, and promotions via a one-year plan. The content is optimized for early career professionals (<5 year’s experience) in need of guidance on how to move up and leaders of early career professionals in need of a framework to help cultivate their talent.
Large language models like GPT-4 and their open-source counterparts provide a leap in capabilities on understanding medical language and context - from passing the US medical licensing exam to summarizing clinical notes. Recently, a wave of health-specific large language models is showing that tuning models specifically on medical data and tasks can result in even higher accuracy on common use cases such as question answering, information extraction, and summarization. Some of these models also aim to address the privacy, hallucination, and fairness issues that current language models exhibit. This session reviews the current state of the art and provides recommendations on what to consider when deploying these technologies in practice.
Hospital beds are an important, finite resource in the healthcare system, and effective utilization of this resource is key to both providing the highest standard of care as well as ensuring operational efficiency. However, the discharge process is often a bottle neck due to the necessity of completing time consuming discharge paperwork as well as the need to identify non-hospital healthcare facilities with capacity to transfer patients that need continuing care. In this case study, we use patient interaction data during the hospital stay to build a predictive model for patient discharge type (defined as both a binary variable and a multi-class classification problem) allowing discharge staff to preemptively begin these time consuming steps in the discharge process. This allows for earlier discharge of patients and more effective utilization of the hospitals resources. While this system has not been implemented at this time, we provide a framework to evaluate the economic value of a predictive model within this context.
Leading healthcare experts will discuss how they see data science in healthcare evolving in the near, mid and long term future.
Defining the problem to solve for, defining the desired outcomes, understanding the type of data involved, and preparing the data appropriately are the four most critical aspects of machine learning in the research space – which will be presented in this session.
Workshops - Thursday, June 22nd, 2023
Full-day: 8:30am – 4:30pm 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.
Full-day: 8:30am – 4:30pm 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.
Full-day: 8:30am – 4:30pm PDT
Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.
In this workshop, participants will get an introduction to generative AI and its concepts and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.