Fierce AI Week

• Ideas to consider         
• Questions to ask         
• Architectural approaches         
• Buy-in from the board         
• Connecting the dots inside a company         
• Real-world applications

Epidemiologists have drawn some parallels between the 1918 flu pandemic and today’s COVID-19 crisis. But today’s disease-fighters have powerful AI tools scientists in the early 20th century could never have imagined. Researchers are using AI to design virus-blocking proteins. Others are using a suite of tools to simultaneously expose the virus’ vulnerabilities and discover drugs that could exploit them. In fact, AI engines have already screened billions of molecules in their hunt for prospective treatments and vaccines. We’ll explore the possibilities—and the progress so far—with experts who are taking AI to the coronavirus fight.

In the rapidly evolving fight against COVID-19, pharmaceutical and healthcare organizations, governments, and the broader scientific communities around the world are working to assess the impact of the virus, and quickly develop fast, accurate solutions.

To get there, researchers and teams must have access to the best, and most up to date, evidence from a broad range of data sources including scientific literature, clinical trials and other textual sources. 

Linguamatics natural language processing (NLP) technology enables fast, systematic, comprehensive insight generation from unstructured text. Sources can include scientific literature, clinical trial records, preprints, internal sources, medical records, social media and news reports. Capturing the key information from these many sources, and synthesizing it into one hub, deepens understanding for our users, which will in turn accelerate insights and outcomes. 

In this webinar, we will discuss the benefits of natural language processing for both COVID-19 insights, and touch on broader applications of this technology across pharma and healthcare.

Decades marketing to physicians has sharpened pharma and agency skills when it comes to doctors, but what about connecting with patients? Effectively reaching and messaging patients—especially those accustomed to dealing with sophisticated consumer marketing companies like Nike and Apple—is easier said than done. That’s where artificial intelligence is making strides. Pharma marketers and their agencies are increasingly adopting AI tools such as chatbots, Alexa voice-activated health skills, natural language processing in call centers and predictive analytics for audience targeting. The result? Better health outcomes for people and a true patient-centered focus for pharma. We’ll discuss the different types of AI tools that pharma marketers and agencies are using with real world examples that highlight both the challenges and opportunities today—and in the future.

Patient-focused drug development requires a strong integration of the patient’s voice at every step of the drug development process. Developing clinical endpoints that are meaningful to patients and that are qualified to measure a relevant change in quality of life is challenging. The patient’s perspective is traditionally investigated with scientific literature reviews, exchange with clinical experts and patient organizations, as well as interviews with patients. However, all of these methods suffer from shortcomings such as underrepresenting the direct view of the patients or representing the view of a limited number of them.

We addressed these shortcomings by using Social Media Listening (SML) as an abundant information source to complement insights from traditional methods. Patients share their perspective not just with their clinicians, but also on social media. They talk about their symptoms and the impacts these symptoms have on their daily lives. SML strengthens the patient-centricity of the drug development process. The level of burden to the patients is significantly lower than in concept-elicitation interviews and it can reach more patients.

Social media data are highly diverse and the analysis of these data comes with significant challenges. One example is the understanding of the patient’s language and its mapping to scientific language. Given the growing amount of data available in social media, robust computational approaches have to be developed to produce meaningful insights.

This webinar will highlight how we integrate the patient’s voice via social media listening studies into the early part of the drug development process. We will give insights into the tasks and tools required for social media listening and how artificial intelligence and natural language processing can help to translate the patients’ voice into actionable insights.

While investment across the biopharma industry in R&D has been consistent or increased over the past 15 years, productivity has remained largely unchanged. Even with the projection that knowledge of medicine is doubling every 18 months, there must be better ways to understand biology and pathobiology and better ways to treat disease and manage healthcare.

Convergence, the result of the sharing of methods and ideas across multiple fields and industries, is the integration of insights and approaches from historically distinct scientific and technological disciplines. At AbbVie, our Convergence initiative seeks to create integrated R&D data and analytics capabilities that power data-driven insights – leading to better, faster decisions and therapies for patients. We are building a foundation for change, and setting out to double R&D productivity.

While hype alarms go off any time someone mentions Artificial Intelligence or ‘AI’, there are boundless opportunities to redefine AI – to engage it as the power of Automation and Insights – and drive value and impact in R&D. Tune in to this keynote to learn how AbbVie data scientists are helping our researchers scale the ladder of inference, how they are helping build foundations for growth and new capabilities, and how they are freeing up our people to perform higher value tasks. Ultimately guided by human empathy and experience, this will drive an increased ability to make a remarkable impact in the lives of patients.

Drugmakers have been working for years to streamline clinical trials, the costly, time-consuming tests required to bring all new drugs and vaccines to market. Enter AI, which can match patients to appropriate trials, manage workflow, analyze incoming data, and help keep participants on track with treatment. These tools can not only save time and money—and flag any problems early—but also increase the chances of success in the clinic. We’ll examine today’s AI tools, discuss how they’re used in the field and find out what’s coming next.

The outcome of a clinical trial is the deciding factor for the future success of a biotechnology and pharmaceutical company. It is therefore important that every critical parameter for a clinical trial is taken into consideration to ensure the highest possible chance of a positive outcome. Using AI and an advanced analytics-enabled engine Innoplexus made it possible to calculate, understand, and predict the outcome of future and ongoing clinical trials using real-world, outside-trial, and biomedical data sets. Hence, enabling companies to choose the best set of parameters for their clinical study.