Agenda

Machine Learning Week Founder Eric Siegel will welcome you to the event and kick things off.

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.

Conference Chair Chris Franciskovich will welcome you to day 2

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.

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.