Full Machine Learning Week 8-Track Agenda 2020 – Detailed Session Descriptions

For years, the insurance industry has been using predicative analytics to identify individual claims that have a high potential for severity so adjusters can manage those claims more strategically.  If we embrace a new and innovative severity-based analytic model to assess and assign all claims at the outset, we can more effectively fuse predictive modeling and expertise. This will require us to change our approach to adjuster caseloads.  The result, however, will be a more efficient allocation of claims resources and the ability to reduce the total cost of risk for clients across an entire book of claims. 

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.

The Safety National Casualty Corporation is the leader in Excess Workers' Compensation.   Predictive Analytics is used internally to identify those claims that it may not see for 7 to 10 years from today.  But, given the low volume of these occurrences, the individualistic nature of each and the potential financial ramifications,  finding the right balance in identification and action is not just a numbers game.

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.

TEMPA - TExt Mining with Predictive Analytics, for Engineering - An approach that allows users to face any unplanned outage in any engineering asset. The asset could be a gas turbine, an aircraft engine, an MRI machine, a locomotive, or a wind turbine. These assets normally provide both descriptive (text) and measured (numerical) output as data - which, when combined properly, have the potential to provide highly actionable insights. TEMPA enables this. This talk would focus on proven methods to automatically extract events from both textual information and operational data, monetize these insights improving profit, as applied in General Electric.

In most organizations, the data science teams and the engineering teams build software separately. This can lead to the development of AI solutions that may not fit into the value delivery pipelines built by the engineering teams. Moreover, the feedback loop to continue learning and improving an ML model becomes challenging with this separation of teams. In this talk, you will learn about an approach of building ML and AI solutions that are developed into micro-services, along with micro front-ends, and deployed into dockerized containers for delivery of quick business value to users.

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.

Data Science in general and Deep Learning in particular continue to reshape the future of the Energy sector across various segments. From exploration, development and production to downstream and new energies business, measurable value of digitalization has been observed in both efficiencies and savings. Deep Learning is one of the key underlying enablers for creating competitive advantage. This presentation provides an overview of some of use case applications and lessons learned from establishing a platform that progress ideas to embedded business enablers.

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time series analysis, stochastic calculus, multivariate statistics, and numerical optimization are often used by "quants" for modeling asset prices, portfolio construction and optimization, and building automated trading strategies.

Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.

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.

The fields of machine learning and artificial intelligence are constantly looking for inspiration to better represent and model the complex relationships of the world. Inspiration can come in many forms, from the human brain inspiring neural networks, to evolution inspiring genetic search algorithms, to groups of animals inspiring swarm intelligence. Swarm intelligence is based on the natural phenomenon of groups of lower intelligence animals working together to accomplish extremely complex tasks. This can be seen in bird flocking and microbial intelligence, but most commonly in ant colonies. Ants are able to communicate complex directions and find optimal travel paths with very little individual intelligence. This concept of many individuals, who by themselves could not solve the problem, working together to find optimal solutions has been adapted into machine learning (ML) techniques. This talk will discuss the usage of a swarm intelligence algorithm, Ant colony optimization, to find optimal contact strategies for introducing new financial products or inquiring about debt collection with customers.

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.

Rexer Analytics has been surveying analytic professionals for over a decade. In 2020, over a thousand people from around the world participated in the 9th Data Scientist Survey. In this PAW session, Karl Rexer will present highlights of recent survey results and discuss trends from the past decade. 

Highlights will include:

1. Trends in algorithm usage, software adoption, and job satisfaction
2. Key challenges reported by today’s analytic professionals 

One of the main components of optimally scheduling portfolio trading is the estimation of the intraday and multi-day propagation of risk and market impact. In this talk, we focus on how machine learning techniques like LSTMs, CNNs, random forests and adaptive splines can improve these estimations for better intraday and multi-day trade scheduling. We also provide examples of how these can be used both in pre-trade scheduling, as well as in real-time optimisation.

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.

There has been an exponential increase in the number of electronic sensors, which now are driving every industry - right from Manufacturing to Aerospace. These sensors are capable of producing a lot of data. However, the sensors are known to be notorious for their jitters. These jitters often result in false alarms which could be troublesome for industrialists. In the session, we will take a deep dive into the state of the art approach. Auto-Encoders are a great product of Deep Learning Neural Networks, if used correctly they have the potential to save lot of cost on the asset.

We continue to hear about machine learning and AI and its disruption within our economy. Data science is no exception as various opinions would suggest the demise of the data science role within a more machine-driven world. In this session, we explore how the need for data science and human intervention has historically always been critical for success in financial services. Yet in a more automated and algorithmic business environment, the need for this human aspect of data science is even more paramount. Case studies and examples will highlight how the human and the machine work together in optimizing the prediction of customer behaviour within financial services.

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.

Advances in additive manufacturing (AM) has allowed metal components to be fabricated quicker, and more cost-effectively than traditional metallurgical processes. For laser powder bed fusion (LPBF) AM, the area beneath the laser turns gaseous (vapor depression) while the area immediately surrounding turns to a liquid (melt pool). Once solidified they form the material microstructure, which determines the performance, lifespan, and physical features of the build. With in-situ data collected by ultrafast x-ray imaging, machine learning can be used to predict these geometries. Additionally, recommendations can be made for the underlying process parameters to determine optimal settings for future build characteristics.

Our clients' interactions generate, on average, nearly ~3 billion rows of data every month. In order to measure and analyze client behavior related to completing servicing needs, we need to construct a client journey across channels for the transaction.  Client journeys map the universe of interactions from all channels to specific servicing needs  For some channels, this is relatively simple, but online behavior is high volume, messy and ever-changing.  

Leaning on the related concepts underlying pointwise mutual information and TF-IDF, we developed a simple machine learning approach to differentiate between online "noise" and online behavior related to a client's intent to complete a transaction. We will discuss how the approach works, and how it allows us to create consistent measurement of the online experience that is automatic, scalable, and detects intended or unintended shifts in the online experience.

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.

Manufacturing has long embraced predictive analytics for process control, but has been slow to adopt active process control in which a black box makes end-to-end decisions for entire process lines. We explore several reasons for this development and compare predict analytics to modern AI based decision making systems, through reinforcement deep learning models. We show that the two are not mutually exclusive and combined can offer a method to overcome the intrinsic limitations of native AI for effective deployment and usage.

No one wants to go to jail. Making decisions based on machine learning models offers new and exciting ways to be convicted -- in court and in the court of public opinion. Models that encode bias and automate discrimination are illegal or at least unethical. Demonstrating the compliance of a model is a hurdle that regulated industries must cross for every model. Even models that seem fine to you and your regulator might sound awful when described in the media. Ethics and compliance are as important to your success as data quality, MLOps, and operationalization. This panel will discuss ethics, bias, regulatory compliance, explainability, and much more to help keep you and your models out of jail.

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.

The latest formula for making sound investment decisions involves mining new alternative data sources, using predictive analytics, swarm intelligence and high-performance computing. It offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships’ positions, credit card transactional data, email receipt data, jobs and layoffs reports, cell phone location data, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street. Applying predictive analytics to these alternative data sources can help discover and contextualize actionable insights that can produce better predictions. Knowing something that only few others know affords firms a competitive edge and positions them better to forge new strategies.

In this talk, Prof. Anasse Bari of New York University, advisor to leading Wall Street firms and co-author of “Predictive Analytics for Dummies,” explains how alternative data sources and predictive analytics are driving value in the world of finance and how swarm intelligence algorithms are reinventing Wall Street. He will explain emerging algorithms, filter fact from fiction, outline successful use cases that he and his team have recently led (e.g. how social performance and consumer reviews could be used as predictive features, how to derive actionable insights from geospatial images.) Prof. Bari will also present an overview that can help you design and implement viable solutions to generate a “predictive analytics-based investment thesis.” 

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.

Research driven companies have a long ranging, unique and amazing data record. A constant challenge is to find relevant data for development scientists and customers to help them solving production problems. Typical approaches span from enterprise scale data lakes to sophisticated numerical simulations. This talk gives insights into use cases that transform a long established manufacturing company into a data-driven supplier.

There are many non-profit organizations trying to help develop and build economic opportunities for developing countries around the world. Vision Fund International uses microfinance loans to jump start business opportunities in these countries. However, these developing countries don't have the credit institutions that countries like the United States have. That leaves these institutions and banks hampered on their ability to make loan decisions. This case study will go through the work provided to Vision Fund to help them develop a credit scorecard model to better predict default of customers to help them make better data driven decisions.

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.