Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Artificial Intelligence, Predictive Modelling and Chatbots: Applications in Pharma

Presentation by Hari Radhakrishnan (senior solution developer) and Josh Mesout (graduate developer), in my team at Deep Learning Summit in London on September 23rd 2016. Brief overview about how we have been exploring artificial intelligence and how predictive modelling has the potential to revolutionise what we do across the drug discovery and development process. Examples include recent exploratory work on AI chatbots and video facial sentiment detection.

  • Soyez le premier à commenter

Artificial Intelligence, Predictive Modelling and Chatbots: Applications in Pharma

  1. 1. Applications in Pharma Artificial Intelligence, Predictive Modelling & ChatBots Hariprasad Radhakrishnan & Josh Mesout Technology Incubation Labs, AstraZeneca
  2. 2. AZ CHATBOT AstraZeneca We are a global, science-led biopharmaceutical business pushing the boundaries of science to deliver life-changing medicines. 61,500 employees worldwide $24.7bn 2015 Revenue* 100+ Countries
  3. 3. AZ CHATBOT Technology Incubation Lab, CTO Hari works as an Solution Architect in the UK Tech Incubation Lab. He loves to bring new emerging technologies into the hands of users Josh is a Developer in the UK Technology Incubation Lab. He develops prototypes and proof- of-concepts with business customers
  4. 4. “By 2020, 85% of customer interactions will be managed without a human” (Gartner) AZ CHATBOT AI is reshaping our world today… AZ CHATBOT
  5. 5. Volumes of Data  Next Generation Sequencing  Whole body imaging  Tissue Microarrays  Sales force optimisation  Clinical trial statistical analytics  High Throughput Screening  Toxicogenomics  Open Innovation Approaches  PowerPoint/Excel content  Structured databases  Predictive Chemistry Modelling  HR employee retention  Real-time news sentiment  experimental data capture  Wearable sensor information  Log analytics in Operations Streaming of Data Variety of Data Complexity of Data We’re a data driven company: We need data driven decisions AZ CHATBOT What does this mean for AZ ? AZ CHATBOT
  6. 6. AZ CHATBOT Predictive Modelling AZ CHATBOT
  7. 7. Developed video extraction  Text AZ CHATBOT PROACT AZ CHATBOT Developed a mobile app to capture patient videos and diaries to understand drug tolerability and potential side effects in phase 1 clinical trials. Developed facial expression  Sentiment
  8. 8. Can we teach it information from the internet? Can we get it returning information about AZ? How about information from our Intranet? Can we apply it to a real use case? Conversational UI will disrupt the landscape in AstraZeneca AZ CHATBOT This is a start for us
  9. 9. AZ CHATBOT Technical Architecture Microsoft Bot Framework Custom Built Natural Language Processing Engine Microsoft LUIS NLP Framework Amazon DynamoDB AstraZeneca Services
  10. 10. AZ CHATBOT Use Cases Help Desk Enterprise Q&A Patient Engagement Drug Information Social Media Interaction Expert Lookup INTERNALEXTERNAL Adverse Events Reporting Finance helpdesk
  11. 11. A Bot to help users dig out useful contacts within AstraZeneca with specific skillsets. AZ CHATBOT Expertise Lookup within AstraZeneca Expert Lookup
  12. 12. Integration with Chatter AZ CHATBOT Chatter Integration Integrating the AI into our Enterprise Social Media
  13. 13. AI uses Natural Language Processing (NLP) to understand everyday speech AI Bots automate everyday tasks Cuts down on unnecessary manpower User submits ticket: Can’t login Bot identifies issue of locked account Bot resets users password AZ CHATBOT Automating the Help Desk Help Desk Bots Undertake Basic Help desk tasks: 1. ChatBots for Service Now 2. Knowledgebase - FAQ 3. Password resets 4. Various form based applications 5. Ordering stuff 6. Handle repetitive tasks so human resources can be put to better use.
  14. 14. • ChatBot should understand the context of the queries and provide information or redirect to the right resources • Support Multiple Languages • ChatBot that understand medical and scientific terminology AZ CHATBOT Increasing Patient Engagement Patient Engagement Social Media interactions & Campaigns Patient advocacy groups Patient Portals Mobile Apps
  15. 15. Question and answer systems A Question and Answer Bot is built using structured FAQ based content that would try answering user questions about AstraZeneca based on the context of users questions. AZ CHATBOT Q&A Systems – Pharma as a Bot
  16. 16. AZ CHATBOT REACH OUT TO US Questions Thank You & hariprasad.radhakrishnan@astrazeneca.com joshua.mesout@astrazeneca.com nick.brown@astrazeneca.com AI Platforms Conversational UI Bot Aggregators ServiceNow Bots Biomedical UI