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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 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.
Workshops - Sunday, June 18th, 2023
Full-day: 8:30am – 4:30pm PDT
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.
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 workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.
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.
In this informal but informative presentation, Dr. James McCaffrey – who leads Microsoft Research’s CEO-mandated initiative to transfer deep learning intelligence into all products, services, and supporting systems across the enterprise – will describe six of the latest trends in machine learning and deep neural systems. Today's hottest trends include the deployment of generative AI systems like GPT-x/ChatGPT, Codex, Copilot, and DALL-E – yet they also include innovative deployments of more classical ML methods. The emphasis will be on information that is practical and can drive revenue, not theoretical ideas filled with math equations and Greek letters. Particular emphasis will be given to explaining the difference between systems that require massive resources or expensive licensing, and systems that can be successfully implemented by organizations with a limited budget
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.
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 a post-pandemic world, technology is helping improve health outcomes, reduce costs, and streamline the administration of health benefits. Craig Kurtzweil, who oversees the use of data for the nation’s largest health care company, will outline how technology is changing when, where, and how people access the health system, to help improve health equity, better address social determinants of health and help prevent disease before it starts, resulting in higher satisfaction rates and lower costs.
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.
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
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.
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.
Predictive Analytics World for Healthcare - Las Vegas - Day 2 - Wednesday, June 21st, 2023
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.
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.
Leading healthcare experts will discuss how they see data science in healthcare evolving in the near, mid and long term future.
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.
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.
