Leveraging Artificial Intelligence and Big Data to Create Value - - PowerPoint PPT Presentation

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Leveraging Artificial Intelligence and Big Data to Create Value - - PowerPoint PPT Presentation

May 22, 2023 206 likes •834 views

Leveraging Artificial Intelligence and Big Data to Create Value Dr. Sudha Ram Director, INSITE Center for Business Intelligence and Analytics Anheuser-Busch Professor of MIS, Entrepreneurship & Innovation Professor of Computer Science

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Leveraging Artificial Intelligence and Big Data to Create Value

Director, INSITE Center for Business Intelligence and Analytics Anheuser-Busch Professor of MIS, Entrepreneurship & Innovation Professor of Computer Science Eller College of Management Email: ram@eller.arizona.edu

  • Dr. Sudha Ram

August 19, 2020 EROSS-2020

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BIG DATA: From Petabytes to ZettaBytes

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Meaning of “BIG”

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Meaning of “BIG”

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Big Data – Traditionally Defined

VOLUME VARIETY VELOCITY VERACITY VALUE

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Diverse Sources of Data

Many Different Sources generating Data

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An Internet Minute

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PARADIGM SHIFT

PARADIGM SHIFT! “Datafication” of the world

Sensors embedded in Physical Objects IP Protocol based communication

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Health Internet of Things

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Paradigm Shift

Temporal and Spatial Dimensions Billions of Users and Objects Leaving Massive Traces of Activity “Laboratory” for understanding the pulse

  • f humanity
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QUE QUEST f for the he HOL HOLY GR GRAIL

Predicting the Future

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INSITE Center for Business Intelligence and Analytics

  • Interdisciplinary Research Center at

University of Arizona

  • www.insiteua.org
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Creating a Smarter/Better World

  • Data Science and Network Science
  • Visualizations Using Time and Space
  • Scalable techniques for network analysis and graph mining
  • Predictive Modeling
  • Train students in Data science
  • Work on interesting research projects with industry partners to

solve real world problems

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RESEARCH PROJECTS

  • Health Care
  • Education
  • News Media/Journalism
  • Crowdfunding
  • Crowdsourcing
  • Internet of Things and Wearable devices
  • Social Media

SOCIAL IMPLICATIONS

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Leveraging Data Science

  • Define a problem/challenge
  • Identify signals
  • Use data science methods
  • Solve the problem

Repurposing Data is Key

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PREDICTION MODELS

Predict Emergency Department Visits in near Real Time Using Big Data  Freshman Retention Prediction  COVID-19 Research

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Leverage Big data

Big Data not just about volume

  • Social media
  • Internet search
  • Environmental sensors
  • Wearable sensors
  • Spatial and Temporal Dimensions
  • Fine Grained - Spatial/Temporal
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Focus on Asthma

  • 25 million people affected in the United States
  • 2 million emergency department (ED) visits
  • 0.5 million hospitalizations
  • 3,500 deaths
  • 50 billion dollars in medical costs annually
  • 11 million missed school days every year
  • 14 million missed work days every year

Source: CDC Reports (2011, 2012)

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Pediatric asthma ER Visits, USA, 2011

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Our Research Objective

Develop Robust Models to predict Asthma Related Emergency Department Visits in near Real Time Using Big Data Partner: Parkland Center for Clinical Innovation Joint work with Wenli Zhang, Dr. Yolande Pengetenze, Max Williams, funded in part by Parkland Center for Clinical Innovation

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Leverage Big data

Big Data not just about volume

  • Social media
  • Internet search
  • Environmental sensors
  • Wearable sensors
  • Spatial and Temporal Dimensions
  • Fine Grained - Spatial/Temporal
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EXTRACTING SIGNAL from Noisy Data

True asthma related tweets Not actually related to asthma

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Asthma Related Tweets

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Asthma Related Tweets

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Asthma Keywords

Asthma Inhaler Sneezing Runny Nose Wheezing

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Asthma Keywords

Asthma Inhaler Sneezing Runny Nose Wheezing

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Asthma-Related Stream

Twitter Asthma Stream - United States

Asthma related tweets, United States, (Asthma stream, 11 Oct, 2013 – 31 Dec, 2013)

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Extracting Signals

  • 1. Tweets indicating awareness of disease,

E.G., “Hope I don’t get an asthma attack again today..”

  • 2. Using disease as rhetoric, e.G., “He is so

cute I think I got asthma” Distinguish tweets that are relevant to asthma from tweets that mentioned asthma in an irrelevant context.

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Emergency Room Visits and Tweets

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Air Quality Sensor Data

  • Identify and include AQI data from a specific

geographic region.

  • Collected pollution data from 27 air quality sites

around the Dallas area.

  • Selected sites closest to the zip codes of the ED asthma

patients in our ED visits dataset. Using this data, we calculated daily average AQI for our model.

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Pollutants

  • CO: Carbon monoxide
  • NO2: Nitrogen dioxide
  • O3: Ozone
  • Pb: Lead
  • PM2.5: Atmospheric particulate matter, diameter of 2.5 micrometres
  • r less
  • PM10: Atmospheric particulate matter, diameter of 10 micrometres
  • r less
  • SO: Sulfur monoxide
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EPA Pollution Sensor Data and Emergency Visits

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Prediction Models Using Streaming Data

  • Air Quality Sensor data streams
  • Tweets
  • Google Trends search data
  • Machine Learning Techniques to predict

number of ED visits per day with high accuracy

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Best Predictors

Successfully predicted with 80% accuracy

  • # of asthma tweets
  • CO
  • NO2
  • PM2.5
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USEFUL for Public Health NOTIFICATION

I. Epidemiologic surveillance of asthma disease activity in the community, e.g., the department of health and human services (DHHS)

  • II. Stakeholders notifications of

community-level asthma- disease activity and risk factors

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Hospital/ED Preparedness

Predicting asthma ED visits and staffing ED consequently

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Targeted Patient Interventions

Targeted patient interventions using patient address and geo-localization data for tweets. E.g., patient alerts about asthma risks and counseling for preventive

methods.

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Contributions

Promising Results Demonstrate the utility and value of linking big data from diverse sources in developing predictive models for non-communicable diseases Specific focus on asthma Relevant for other chronic conditions – Diabetes, Cardiac problems, Obesity

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Internet of Things and Big Data

Big Data for Improving Education Internet of Things: Smart Cards, Wifi Logs, Mobile Apps

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BUILDING A SMARTER CAMPUS

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Combining Network Science and Machine Learning

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Societal Challenge: Student Retention Proactive Prediction is very Important Social Science theories indicate:

  • Social Interactions
  • Regularity of Routine
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Objective

Predict freshman retention at individual level Make proactive prediction before knowing first term GPA Learn students’ behavioral patterns from their CatCard transactions Provide actionable suggestions for retention management

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BIG DATA

Institutional Student Dataset ~ 7000 full-time registered freshmen, 6500 are left after removing international students for whom SAT scores or high school GPAs were not available 479 (7.37%) drop-out after Fall and 843 (12.98%) drop-out at the end of Spring SmartCard Transaction Dataset 1.8 million transactions made by freshmen from Aug 2012 thru May 2013 271 different locations include restaurants, vending machines, printers, parking, labs.

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Behavior and Interactions

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Patterns and Differences

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Movement and Behavior

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COMPUTATIONAL and NETWORK SCIENCE APPROACH

Fills gaps in behavioral and extant data-driven approaches New prediction approach CatCard transactions  implicit social networks and spatial sequences Proactive prediction Predicting retention before the end of 1st semester with 90% recall

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COVID-19 Related Research Projects

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 What is Contact Tracing?  Digital vs. Manual Methods  Three Different methods

  • a. Manual contact Tracing
  • b. Manual with Digital assistance from Prompted Mobility Pathway

aka Memory Jogger

  • c. Digital: BlueTooth App for exposure notification

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Memory Jogger using Wifi Logs

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 Working with Jeremy Frumkin, Research and Discovery

Technologies

 Using Wifi network logs with Catcard data to support strategic

efforts related to congestion tracking on campus and managing campus foot traffic

 Understanding Movement Patterns among Campus spaces  Complementing app-based and manual contact tracing efforts with

the additional insights that can be gained through the wifi logs.

 Design a Memory Jogger – prompted Mobility pathway tool to

enhance manual contact tracing

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Traffic/Crowd Analysis

Select Date: Feb 3, 2020 Time 8 am-9 am Building

User types

Traffic on campus between 8am and 9am Top ten traffic spots visualized and compared with selected building (in red) Comparison of hourly Traffic in selected building

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 To compare the three methods for Contact Tracing and Exposure

notification.

 How do the three contact tracing approaches differ in their outcomes such

as timeliness and coverage of contacts and other metrics?

 How do these methods complement each other and what are their relative

strengths and weaknesses?

 How do these methods perform overall in preserving privacy while allowing

for comprehensive contact tracing? What are the tradeoffs?

 How acceptable are these three strategies to the community and what is

an effective path to deploying comprehensive contact tracing?

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Some General Lessons

  • Need for complex techniques?
  • Is causality really necessary for prediction?
  • What level of accuracy is good?
  • Working with your stakeholders is important
  • Research is very important in training next

generation scientists, end users, students,

  • thers
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Some General Lessons

  • Focus on defining the problem carefully
  • Out of the Box thinking
  • Big Data: Don’t think of it as a single very large dataset
  • Repurpose and combine different types of data
  • Exploit the granularity of data especially the spatial and

temporal features: Machine learning and network science

  • Extracting Signal from Noise
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Good News

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McKinsey in 2015: predicted that by 2020 the number of data science jobs in the United States alone will exceed 500,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists was projected to exceed supply by more than 50 percent by 2020. IBM today: Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.

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CONCLUSION

  • PARADIGM SHIFT
  • BIG DATA HAS A LOT OF HIDDEN VALUE
  • LET’S LEVERAGE IT USING AI TO CREATE A

BETTER WORLD!

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QUESTIONS??

TEDx Talk:

http://tedxtucson.com/portfolio/sudha-ram/ www.insiteua.org Email: ram@eller.arizona.edu

Twitter: @sudharam