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April 20, 2020

Cal State LA Introduces COVID-19 Dashboard, AI-Powered Mortality Risk Prediction Tool

COVID-19 is producing a deluge of data, from cases and hospitalizations to ventilator supplies and protein forms. Researchers at Cal State LA are leveraging that data, producing two tools: an interactive visual dashboard showing the predicted progression of COVID-19 in specific areas and an AI model that estimates mortality risk for COVID-19 patients.

The creators of the interactive dashboard, who work in Cal State LA’s College of Business and Economics, were inspired by the COVID-19 dashboard created by Johns Hopkins University early in the pandemic. Seeing room to simplify the dashboard and enable easier comparisons, they used Tableau to create a map that allowed users to filter to specific states and view forecasted cases and deaths. The dashboard, which is updated daily, uses data from the Johns Hopkins Center for Systems Science and Engineering.

“I believe that help can be found on different scales and from different domains, and even a small opportunity to help during this outbreak means a lot to me,” said Dalya Dauletbak, a recent Cal State LA graduate who now works for the school as an information security data analyst and helped to create the dashboard. “Keeping people informed with the correct data is one of the keys to flattening the curve.”

Dauletbak works at Cal State LA’s Big Data AI Center, led by Professor Jongwook Woo. “Technology has developed to make the lives of human beings easier and safer,” Woo said. “I am proud that the Big Data AI Center can use big data to contribute to the community quickly with the knowledge that people need now in this crisis.”

To access Cal State LA’s COVID-19 dashboard, click here.

Elsewhere, at Cal State LA’s College of Engineering, Computer Science, and Technology, Professor Mohammad Pourhomayoun and graduate student Mahdi Shakibi created a different kind of predictive tool. Using AI and machine learning algorithms, they developed a model that estimated mortality risks for COVID-19 patients. The model pulled symptomal, physiological, and demographic data from 117,000 patients around the world and demonstrated a 93% prediction accuracy in testing. 

“We have to remember that the main players and the real heroes are our doctors, nurses, and all health care workers risking their lives to save people on the front lines of the coronavirus fight,” said Pourhomayoun. “But I think everyone with any expertise can try to help. Every researcher in every field of research – whether it is medicine or biology or computer science or engineering or social science – can contribute to help address the COVID-19 crisis.”

The researchers hope that the model will be used to help hospitals prioritize patients at greater risk, and they have already begun discussions with government and health officials about deployment of the model. 

“I commend our faculty, students, and staff for their continued dedication to engagement and service for the public good during this difficult and uncertain period,” said William A. Covino, president of Cal State LA.