Big Data and AI in Fighting Against COVID-19
- 1. Big Data and AI In Fighting Against Covid-19 -- Andrew Zhang [email protected] 7/8/2020
- 3. 1. Introduction 2. Supercomputers for Scientific Research 3. Covid-19 Open Data Lake 4. NLP and BERT to answer scientific questions Agenda
- 4. Speaker: Andrew Zhang Senior Tech Acct Manager at AWS, his specialties are big data, machine learning, and HPC. Before joining Amazon, Andrew was a data science engineer with IBM. His interest is scaling machine learning in a hybrid multi-cloud enterprise environment. Previously, Andrew was an enterprise architect with Novartis Pharmaceuticals.
- 6. 6 Supercomputers for Scientific Research
- 7. Extensive research in bioinformatics, epidemiology, and molecular modeling to understand the treatment and develop strategies Bringing together leaders to provide access to the world’s most powerful high- performance computing resources. Covid-19 High Performance Computing Consortium
- 8. Covid-19 High Performance Computing Consortium https://mit-satori.github.io/
- 9. Covid-19 Active Research Projects “We have identified two target proteins that generate novel molecules to inhibit the relevant proteins. The compute capacity will enable us to run and optimize our neural networks to generate better molecules and estimate their binding affinity to the target proteins, drug-likeness and ADMET properties. Our work will evolve to use 3D SMILES (currently at 2D) and other improvement.” “ We are working with a team who have developed a device to allow safe ventilator splitting between 2 or more patients. We made the software to guide device selection based on the patient's respiratory states, but we want the app to allow for just lookup into pre-computed values from the …” We have designed a mobile app and a technological platform, compliant to the European legislation, which enables unidentified contact/exposure information of users to be efficiently collected in a fully anonymous way. https://covid19-hpc-consortium.org/
- 10. 10 Covid-19 Open Data Lake
- 11. Covid-19 Tracking and Prediction COVID-19 confirmed cases and deaths Genomic epidemiological tracking Hospital resource utilization modeling This is a visual representation of the number of confirmed cases (counties) and deaths (circles). Data Source: COVID-19 data sources: the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE. Genomic epidemiology of novel coronavirus which provides real- time tracking of pathogen evolution (click to play the transmissions and phylogeny) Hospital resource utilization modeling Data Source: University of Washington’s Institute of Health and Metrics Evaluation (IHME) COVID-19 projections. Source: DataBricks
- 12. Covid-19 Research and Diagnosis Answer Key Questions from Scientific Literature Read COVID-19 X-ray or CT image • What is known about transmission, incubation, and environmental stability? • What do we know about COVID-19 risk factors? • What do we know about virus genetics, origin, and evolution? • What has been published about medical care? While PCR tests offer many advantages they are physical things that require shipping the test or the sample. X-ray machines can be plugged in to screen patients as long as they have electricity. AI tools can help general practitioners to triage and treat patients. Companies are developing AI tools and deploying them at hospitals Wired 2020. Source: IEEESource: Kaggle
- 13. Open Data Lake: Query and Visualization (Amazon) • Global Coronavirus (COVID-19) Data – Tracks confirmed COVID-19 cases in provinces, states, and countries across the world with a breakdown to the county level in the US. • Coronavirus (COVID-19) Data in the United States – Tracks confirmed cases and deaths in the US by state and county. • Coronavirus Disease (COVID-19) Testing Data – Tracks the number of people tested, pending tests, and positive and negative tests for COVID- 19. • USA Hospital Beds – COVID-19 – Data on hospital beds and their utilization in the US. • COVID-19 Open Research Dataset (CORD-19) – A collection of over 45,000 research articles (over 33,000 with full text) about COVID-19, SARS-CoV-2, and related coronaviruses. AWS has preprocessed and enriched these with annotations extracted from Amazon Comprehend Medical. • Amazon: S3 Explorer https://dj2taa9i652rf.cloudfront.net/ • Amazon: Glue Simple Cost Effective ETL https://aws.amazon.com/glue/ • Amazon: Athena a serverless SQL query engine https://aws.amazon.com/athena/ • Amazon: QuickSight https://aws.amazon.com/quicksight/ A public data lake for analysis of COVID-19 data | AWS Big ... QuickSight Dashboard
- 14. Open Data Lake: Query and Visualization (Google) COVID-19 data from Johns Hopkins Center for Systems Science and Engineering OpenStreetMap Public Dataset : World map including healthcare provider locations Global Health Dataset from The World Bank : Global health and population trends asked questions and tips to get started. New York Times COVID-19 database: The New York Times' COVID-19 database based on US health agency reports. ECDC COVID-19 Cases by Country : COVID-19 cases by country as reported by the European Centre for Disease Prevention and Control. USAFacts COVID-19 Cases by US County : COVID-19 cases by county aggregated by USAFacts from US health agencies. Big Query
- 15. 15 NLP and BERT to answer scientific questions
- 16. 16 NLP and BERT Source • BERT, as a contextual model, captures these relationships in a bidirectional way. • I made a bank deposit the unidirectional representation of bank is only based on I made a but not deposit. • The pre-trained model on massive datasets enables anyone building natural language processing to use this free powerhouse. • BERT theoretically allows us to smash multiple benchmarks with minimal task-specific fine- tuning. • Corporate data to create different application.
- 17. 17 COVID-19 Open Research Dataset Challenge https://www.whitehouse.gov/briefings-statements/call-action-tech-community-new-machine-readable-covid-19-dataset Every scientist working on a cure or vaccine must understand this prior research. 158,000 Coronavirus scholarly articles including 75,000 with full text. • What is known about transmission, incubation, and environmental stability? • What do we know about COVID-19 risk factors? • What do we know about vaccines and therapeutics? • What do we know about virus genetics, origin, and evolution? • What has been published about medical care? • What has been published about ethical and social science considerations? • What do we know about non-pharmaceutical interventions? • What do we know about diagnostics and surveillance?
- 18. Explore Covid-19 Scientific Literature (1) Generate Summaries from Abstracts by training Summarizer Model Databricks Generate a WordCloud from all the titles
- 19. Explore Covid-19 Scientific Literature (2)All Task/Challenges answers using NLP: We will use different libraries to get answer from these papers. • Bert QA Model (Pretrained by SQuAD dataset) • BERT summary Model • Python Google translate package • HTML for visualize result All Flow: • Using QA Model, read all paper's abstract then find answer for all tasks • Concatenate Top 50 confident answers to be article, and using Summary model to write summary of answers • Translate multiple language by google translate • Write HTML to show summary of all ‘papers answer for all tasks. Kaggle
- 20. 20 Explore Covid-19 Scientific Literature (3) Google 1. When the user asks an initial question, the tool not only returns a set of papers (like in a traditional search) but also highlights snippets from the paper that are possible answers to the question. 2. The user can review the snippets and quickly make a decision on whether or not that paper is worth further reading. 3. If the user is satisfied with the initial set of papers and snippets, we have added functionality to pose follow- up questions, which act as new queries for the original set of retrieved articles.
- 21. 21 Explore Covid-19 Scientific Literature (4) Amazon AWS COVID-19 knowledge graph (CKG) using AWS CloudFormation and Amazon Neptune, and query the graph using Jupyter notebooks hosted on Amazon SageMaker in your AWS account. The CKG aids in the exploration and analysis of the COVID-19 Open Research Dataset (CORD-19), hosted in the AWS COVID-19 data lake. The strength of the graph comes from the connections between scholarly articles, authors, scientific concepts, and institutions. The CKG also helps power the CORD-19 search page..
- 22. Questions ? Twitter: @a9zhang Email the speaker: [email protected]