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The current AI hype is a result of a series of breakthroughs in the research of deep neural networks. Many of the new, so-called deep learning techniques were initially invented and used to solve challenges in biochemistry and medicine, e.g. for gene detection. Despite its origin, nowadays AI is often associated with marketing automation or autonomous driving. These cases are tangible and easy to understand. However, many critical and relevant applications are deployed in healthcare, pharmacy and life sciences: they are not only cost- but often life-saving. By bringing the successful PAW Healthcare conference series to Munich, we will provide a regional platform for the European data science community to share their success stories and insights with their industry peers. Two well established conferences, PAW Industry 4.0 and Deep Learning World, are running parallel in the same venue. Don’t miss these two days, that will provide the perfect platform for in-depth knowledge-sharing, interactive, expert discussions and intensive industry networking.
Attend Predictive Analytics World for Healthcare and witness today’s rapidly emerging movement to fortify healthcare with big data’s biggest win: the power to predict. The premier cross-vendor networking event, this conference assembles the industry’s leaders to deliver case studies and expertise, revealing how predictive analytics:
Predictive analytics addresses today’s pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions:
Personalized medicine. Per-patient prediction and analytically enhanced diagnosis drives individual clinical treatment decisions
Insurance. Predictively guided decisioning combats risk and renders insurance more equitable and profitable
Hospital administration. Analytics detects and recoups loss due to fraud and waste
Healthcare marketing. From medical suppliers to healthcare screening service providers, the performance of industry enterprises hinges on analytically targeted marketing
Drug development. Analytics advances pharmaceutical engineering, testing, and other processes
Much more. Other applications include predicting per-patient disease progression, mortality risk, availability of clinical trial participants, consumer prescription adherence, and more
PAW Healthcare provides unique learning and networking opportunities for physicians, medical researchers, administrators, marketers, and analytics professionals from:
Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.
Predictive Analytics World 4.0 often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
However, Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
Yes. Predictive analytics means the commercial deployment of machine learning (the two terms are often used synonymously). Although the term “machine learning” used to be common only within the walls of research labs, it’s now also used more and more in the context of commercial deployment. Whichever term you prefer, we are discussing technology that learns from data to predict or infer an unknown, including decision trees, logistic regression, neural networks, and many other methods.
Yes. Data mining is often used synonymously with predictive analytics, and, in any case, predictive analytics is a type of data mining.
Yes. Predictive analytics is a form of data science. Moreover, it is the most actionable form. A predictive model generates a predictive score for each individual, which in turn directly informs decisions for that individual, e.g., whether to contact, extend a retention offer, approve for credit, investigate for fraud, or apply a certain medical treatment. Rather than solely providing insights, predictive analytics directly drives or informs millions of operational decisions.
Yes. Predictive analytics is a key method to truly leverage big data. At the center of the big data revolution is prediction. The whole point of data is to learn from it to predict. What is the value, the function, the purpose? Predictions drive and render more effective the millions of organizational operational decisions taken every day.
Yes. Artificial intelligence (AI) is a broad, subjective term with many possible definitions—but by any definition, it always includes machine learning (predictive modeling) as an example of AI technology/capabilities.
No. Predictive Analytics World provides a balanced view of predictive analytics methods and tools across software vendors and solution providers.
No. Predictive Analytics World is focused on today’s commercial deployment of predictive analytics, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive analytics’ commercial application, we are essentially standing on the shoulders of those giants known as researchers.
For speaker information and proposal submissions, click here.