Predictive Analytics World for Healthcare 2020
May 31-June 4, 2020
Click here to view the full 7-track agenda for the five co-located conferences at Machine Learning Week (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).
Pre-Conference Workshops - Sunday, May 31st, 2020
Full-day: 8:00am – 3:00pm
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.
Full-day: 7:30am – 3:30pm
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.
Pre-Conference Workshops - Monday, June 1st, 2020
Full-day: 7:15am – 2:30pm
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: 8:00am –3:00pm
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. Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it. Click workshop title above for the fully detailed description.
Predictive Analytics World for Healthcare - Las Vegas - Day 1 - Tuesday, June 2nd, 2020
A veteran applying deep learning at the likes of Apple, Bosch, GE, Microsoft, Samsung, and Stanford, Mohammad Shokoohi-Yekta kicks off Machine Learning Week 2020 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coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated
As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven't even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google's AI Principles.
As the economy continues its uncertain path, businesses have to expand reliance on data to make sound decisions that directly impact the business - from managing cash flow to planning product promotion strategies, the use of data is at the heart of mitigating the risks of a recession as well as planning for a recovery. Predictive Analytics, powered by Artificial Intelligence (AI) & Machine Learning (ML), has always been at the forefront of using data for planning. Still, most companies struggle with the techniques, tools, and with lack of resources needed to develop and deploy predictive analytics in meaningful ways. Join dotData CEO, Ryohei Fujimaki to learn how automation can help Business Intelligence teams develop and add AI and ML-powered technologies to their BI stack through AutoML 2.0, and how organizations of all sizes can solve the predictive analytics challenge in just days without adding additional resources or expertise.
Mike will address the topic of the observed effect of the COVID-19 public policies that have reduced the mobility of countries around the world. With global mobility data joined to case data he will quantify when and by how much the lock-downs have mitigated the spread of this novel disease. The original rationale for social distancing was to mitigate peak loads on health care service delivery and buy time for the development of treatments. While the early reductions in mobility had some of their intended effect, the evidence of mitigating the COVID-19 spread shortly before and then beyond the peak has dropped to zero. The drivers of this phenomenon and public health policy implications will be discussed.
Healthcare organizations make significant investments in care interventions (e.g., care management) but are often unable to determine if the intervention had an impact on cost and utilization metrics (e.g., PMPM, admissions). This is due to an inability to perform an analysis that captures the true effect of an intervention. Front Health developed an Impact Tracker using propensity score matching and pretest-posttest cohort analysis to measure the significance of the effect that clinical interventions had on patient outcomes. We’ll discuss how the Impact Tracker measured the impact of Annual Wellness Visits on patient outcomes in a Medicare Shared Savings Program.
Physicians should be lining up, reaching over each other to get their hands on the AI that can help them deliver better care, right? This same line was said about the promise of the EHR, and today only 40.3% of physicians agree that their EHRs alerts prevent mistakes (source: KLAS Arch Collaborative, n = 10,938). Why are these technologies not taking hold? Join Taylor Davis, KLAS Research’s VP of Research, as he shares findings from the KLAS Arch Collaborative and the KLAS 2019 AI Study. Learn how we can better develop these technologies with physicians, delivering them for physicians (instead of to physicians).
Ari Kaplan will talk about his real-life Moneyball experiences of disruption in Major League Baseball front offices - and how artificial intelligence will disrupt every business industry. Having helped lead the adoption of data science throughout baseball, including creating the Chicago Cubs analytics department, he will lead lively discussion on how winning in baseball translates to winning across other industries, overcoming cultural resistance, and doing analytics at scale and velocity to win the race.
Join Vickie Rice, VP of Strategic Analytics for CareATC, to learn how this employer-sponsored primary care company
is using cognitive machine learning to shape its response to COVID-19. From predicting where resources will be needed and finding the best locations to establish pop-up testing sites to identifying those in their population that are most at risk for mortality
and morbidity if infected, CareATC is relying on AI to make key decisions in shaping its response to this healthcare crisis. In this session, we’ll review the predictive models utilized and learn about the practical applications that turned this science into
true life-saving measures.
Predictive Analytic tools and techniques have revolutionized and improved outcomes in multiple industries. Yet, within the Healthcare industry there can be multiple barriers to overcome.
Technical barriers can slow model deployment, cultural barriers can impede communication of results, and rare events can make it difficult to show significant improvement over a typical pilot project timeframe. Learn how the University of Virginia Health System has developed a real-time predictive analytics visualization platform, used interpretable machine learning techniques to communication results, and developed a novel upstream improvement metric to speed process improvement.
Blood, platelets and other transferable fluids are critical for patient health –particularly during trauma treatment, surgery and chemotherapy. Every day OneBlood collects, processes and delivers thousands of units of blood and blood products; keeping hospitals throughout the Southeastern US ready to provide life-saving healthcare. OneBlood is using predictive modeling to better understand and to shape donor behavior. Additionally, by forecasting hospital blood product needs, OneBlood is able to anticipate demand and modify donor recruitment and the processing of blood products. This ensures that hospitals have the right blood products in the right amounts at the right time.
Predictive Analytics World for Healthcare - Las Vegas - Day 2 - Wednesday, June 3rd, 2020
Rick Hinton will discuss how organizations need to rethink existing organizational change management practices to improve analytics project success. Drawing upon examples from healthcare and other industries, he will talk about a new definition for change management, key impediments to change and their impact, the unique challenges of analytics projects, and a new model for change management ideally suited for analytics-focused initiatives.
Many data science teams are working in environments where we're being asked to produce higher quality predictive models quickly with fewer resources. This demo will show how DataRobot's platform allows data scientists and analysts alike to build models using image and tabular data
In-hospital and out-of-hospital mortality and readmission prediction models have been extensively covered in literature intended for scientists and practitioners. Adaptive development that feeds real-time performance data back into model calibration has received much less attention. This presentation shows how operational decisions influence the structure of the developed framework and how models are affected by implementation decisions. Aligning the operational and predictive analytics functions helps create a unified scalable multi-purpose prescriptive analytics framework within the organization. The data science function can help solve the operational challenges arising from such implementation by using additional analysis obtained through continuous monitoring of live data.
Many diseases and cancers produce cell metabolites that accumulate in body tissues and fluids beyond normal ranges. These metabolites, which include various volatile organic compounds (VOCs), can be considered biomarkers for disease. These volatiles can be analyzed in blood plasma, and some studies have shown particular VOCs to correlate with the presence of various cancers and diseases.
VOCs dissolved in plasma samples of a group of normal people and cancer patients were analyzed for constituency with a gas chromatograph-mass spectrometer (GCMS). The GCMS output data were modeled with machine learning technology. Analysis outputs were preprocessed with a Python script to identify the peaks of interest. Output data were normalized to account for systematic error in the GCMS operation, and input to a KNIME workflow for prototype modeling. A model was trained on ion count peak data from an equal number of lung and pancreatic patients, plus an equal number of normal patients. Final data preparation logic was completed and the model was trained in the KNIME Platform. The workflow was converted to Python and used to predict cancer presence in the plasma samples through a Random Forest algorithm found in the scikit-learn (sklearn) library. The model predicted the correct cancer status of a plasma sample 93% of the time.
This case study confirms many reports in the literature that VOCs can be correlated with cancer presence. These correlations may one day be considered as basis of a diagnosis, but for now they could be used as the basis for ordering further tests and biopsies to make a diagnosis.
A disease detection system is being developed by Volatile Analysis Corporation as a commercial application for VOC’s in a machine learning environment.
Clinicians, and especially doctors, train for many years to develop expertise in the diagnosis and care of patients. Further intuition and experience gained from years of practice makes them particularly effective at diagnosing and treating patients where there is a clear diagnosis and a well-defined and accepted course of treatment. Artificial intelligence and predictive models promise to provide guidance where there is ambiguity and uncertainty. But, following a course of treatment that is guided by data analysis and machine learning can feel like “cookbook medicine” for some clinicians, whose first reaction is to push back against it. There are also legal, ethical, and philosophical considerations that come into play with use of an impersonal system that affects health care decision making.
The expert panel will explore the current state and trends in the adoption of machine learning among doctors and other clinicians. The panel will debate obstacles to the adoption of artificial intelligence and consider ways in which the wisdom of clinicians gained from years of training and practice can be combined with predictive modeling to achieve better outcomes for patients.
There will be ample time to ask questions of the panel members.
This talk will showcase how Geneia is using low dimensional representation of medical concepts to more effectively identify similar patients. The use of this technique allows health plans, hospitals and provider organizations to better identify and engage patient cohorts.
This talk overviews Quantopo's efforts to understand the anatomy of an Ebola outbreak and predict how an outbreak will change under different responses. This work extends previous work on the 2014 West African Ebola outbreak and operational support in Mali. Results presented in this talk were used to support disaster response ground operations in the Democratic Republic of the Congo and position workers in areas likely to be hit the hardest in the weeks after the analysis. Regrettably, operational support was stymied by violence against aid workers and lack of international funding for a swift response.
Algorithms are used in many high-stakes decisions that affect our daily lives. As we develop AI systems that automate decisions in healthcare, how do we ensure trust and fairness? In this talk, we will discuss how to address algorithmic bias in building trusted AI systems. We will start with an introduction of various components of trust: fairness, accountability, transparency, and ethics. We will discuss various case studies to illustrate how fairness sneaks into algorithms. We will then focus on the tools & techniques that practitioners can use to detect, mitigate and monitor bias in AI applications.
By the end of 2018, researchers had debunked a theory paper from our lab and concluded that feelings of fatigue did not cause changes in gait and posture. Fortunately for our lab, this was not the end of the road, but the beginning of new discoveries and a new line of inquiry. This disproven theory was the origin of the path to discover a new field of research. Because of this disproven theory, we re-examined previous data and identified that energy and fatigue as distinct moods. This led us to conduct experiments where we find that energy and fatigue have distinct influences on expression of various physiological, immunological and biomechanical characteristics. In this talk we will discuss, how we need to think outside of our own expertise and learn from failure.
Given the vast amount of unstructured data available to health care payers in various forms –ranging from clinical records to audio recordings –the need to build out and apply a natural language processing (NLP) platform is essential. This session will discuss the foundational strategy and components being developed at CDPHP, a mid-size health care payer in New York’s Capital Region. Real-world use cases will be discussed, focusing on increasing efficiency and accuracy, all while reducing error. There will also be discussion of applications focused on member insights and experience. Practical examples will be taken from HEDIS reporting, risk revenue, and member engagement.
Post-Conference Workshops - Thursday, June 4th, 2020
Full-day: 7:15am – 2:30pm
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: 8:00am –3:00pm
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: 8:00am –3:00pm
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: 8:00am –3:00pm
During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) at this one-day workshop, scheduled the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences. Click workshop title above for the fully detailed description.