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Databricks targets retail vertical with its first industry-specific lakehouse

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Did you miss a session from the Future of Work Summit? San Francisco headquartered Databricks, a company that offers the capabilities of a data warehouse and data lake in a single "lakehouse" architecture, today announced its first industry-specific offering: Lakehouse for Retail. Designed specifically for enterprises dealing in the retail and consumer goods vertical, Databricks says Lakehouse for Retail is a fully integrated platform that aims to solve the most critical challenges retailers and their partners face while trying to leverage surging data volumes for AI and analytics projects. The solution, which is generally available as of today, has already seen early adoption from major retail enterprises including Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51, Co-Op Food, Gousto, and Acosta. "With hundreds of millions of prescriptions processed by Walgreens each year, Databricks' Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads," said Luigi Guadagno, the VP of pharmacy and healthcare platform at Walgreens.


AI Is Ready To Impact Clinical Trials

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Artificial Intelligence (AI) is making its way into the realm of clinical trials. While most of the talk I hear seems to center on clinical trial recruitment and using AI to mine electronic medical records (EHRs), that application seems to only scratch the surface. Experts predict monitoring drug adherence, pre-emptive risk monitoring, decision-making, diagnostics, and process optimization are other areas where the technology is expected to make an impact. By the middle of 2020, the AI market for healthcare is expected to top $35 billion, and big names such as Microsoft, Google, and IBM are already collaborating with top universities to further AI. We engaged experts from four of the largest companies in the industry to provide insights on the implementation of AI in clinical trials and the challenges companies are facing.


How to Build Scalable Real-time Applications on a Databricks Lakehouse with Confluent

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For many organizations, real-time data collection and data processing at scale can provide immense advantages for business and operational insights. The need for real-time data introduces technical challenges that require skilled expert experience to build custom integration for a successful real-time implementation. For customers looking to implement streaming real-time applications, our partner Confluent recently announced a new Databricks Connector for Confluent Cloud. This new fully-managed connector is designed specifically for the data lakehouse and provides a powerful solution to build and scale real-time applications such as application monitoring, internet of things (IoT), fraud detection, personalization and gaming leaderboards. Organizations can now use an integrated capability that streams legacy and cloud data from Confluent Cloud directly into the Databricks Lakehouse for business intelligence (BI), data analytics and machine learning use cases on a single platform.


Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media

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Safety surveillance in the premarket clinical trial process is designed to identify common adverse events (AEs) and drug reactions (ADRs) occurring in study populations. Typical clinical development programs include sample sizes between a few hundred to several thousand study patients in total; allowing for identification of AEs occurring between approximately 3% (i.e., 3/100) down to 0.3% (i.e., 3/1000) by the'Rule of Three' [1]. However, many subsequent ADRs are identified after the drug is on the market due to factors including exposure to an expanded patient population, concomitant medication use, dosing patterns, off-label usage, and intentional misuse [2, 3]. Effective postmarket pharmacovigilance, defined by the World Health Organization as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem" [4], relies on swift, accurate, and comprehensive reporting of ADRs through the submission of individual case safety reports (ICSRs) to the appropriate regulatory bodies. A streamlined, global approach to pharmacovigilance increases the power of signal detection activities to identify and supplement the initial safety profile based on randomized controlled trials in the area of clinical safety. This is facilitated by the application of the revised Guideline for Clinical Safety Data Management: Data Elements for Transmission of ICSRs (E2B), which was developed by the International Council for Harmonization and has been widely adopted as the standard for ICSR reporting [5].


How AI Can Transform Pharmacovigilance And Monitor Safety Of Medicinal Products

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Nicole Baker who is the co-founder of biologit, an early-stage technology startup using artificial intelligence solutions for pharmacovigilance and clinical safety, took us through an interesting session on why AI is needed for monitoring safety of medicinal products at Rising 2020. Baker who started as an immunologist soon realised that there is a lot of data to read through and that's when she explored the use of artificial intelligence to bring about ease and efficiency in her pharmacovigilance work. For the uninitiated, pharmacovigilance is the science and activities relating to detection, assessment, understanding and prevention of adverse effects or any other medicine-related problems. Before a medicine is authorised for use, evidence of its safety and efficacy is limited to the results from clinical trials, but after the medicine goes into public use, there can still be cases of adverse drug reactions which can be reported by doctors, nurses, and even users themselves. Pharmacovigilance involves ensuring that the patient is safe and that the medicine is not causing adverse reactions.