Leveraging AI for Real-Time COVID-19 Discovery to Improve Clinical Outcomes

The COVID-19 pandemic illuminated the medical field's need for intuitive Knowledge Discovery Systems, capable of answering patient-asked or clinician-asked COVID-19 questions with a high degree of statistical accuracy. One of HPE's many contributions to the COVID-19 relief effort included a new AI-driven Schema-less Data Mining pipeline, capable of driving real-time COVID-19 knowledge discovery to improve clinical outcomes.

During this session we will share our insights about the following processes and Machine Learning algorithms used in each:

Data pre-processing steps using Kafka, Flink, and Aerospike
Data post-processing steps using Python, NLTK and Spacy Natural Language Processing Libraries
Data visualizations using Python, R Studio and Vertica

Theresa Melvin

Global Chief Architect, BD/AI Solutions
HPE

Bio info: Theresa Melvin, J.D., serves as Chief Architect of AI-Driven Big Data Solutions for HPE's Open Source Profession and...

Theresa Melvin, J.D., serves as Chief Architect of AI-Driven Big Data Solutions for HPE's Open Source Profession and Worldwide Solutions Group. After years of custom consulting, she joined HPE in 2012, and eventually rose to lead the technical team that created HPE's Open Source Solutions Program. Speaking several languages and possessing an array of degrees and technical mastery certifications, Ms. Melvin develops End-to-End AI-Driven solutions founded on Open Source and Community Supported Software. These solutions assist the hyperscale and extreme-scale market in significantly reducing their infrastructure footprint while meeting their sub-second Service Level Requirements, at the best price-point possible.