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Using data analytics to improve disease prediction

Lehigh CSE professor Mooi Choo Chuah co-chairs CHASE Conference in Philadelphia

Big Data holds great promise to change healthcare for the better. But its potential will not be reached until healthcare providers improve the efficiency with which data is shared and the accuracy with which it is interpreted.

The Second IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies brought experts from academia, business and government together to share information and help accelerate health care’s transformation. The international conference is was held from July 17-19 in Philadelphia, Pennsylvania

Mooi Choo Chuah, professor of the computer science and engineering department at Lehigh University, served as the conference’s technical co-chair, along with Insup Lee of the University of Pennsylvania. Chuah conducts research in next-generation wireless network architecture design, network and Smart Grid security, and mobile and cloud computing. She has recently begun to investigate healthcare data mining.

Chuah, the co-director of the technical program committee planning the conference’s content, also presented paper, “Incentivizing High Quality Crowdsourcing Clinical Data for Disease Prediction.”

Her group’s recent research offers two contributions, says Chuah. The first, an approach she developed with her graduate student, Qinghan Xue, uses a large dataset to demonstrate an improved disease prediction model that combines data cleaning and careful feature selection with effective machine learning techniques.

The paper’s second contribution presents a solution to one of the major challenges of healthcare: the fact that no single hospital or health care system has enough of its own data for useful predictive disease analysis.

“Hospitals and other health care systems collect troves of data,” says Chuah. “However, each has a limited number of patients experiencing a particular disease—such as ALS or diabetes, for example. We have designed an incentive method to encourage hospitals to share data so that better prediction models can be created.”

Chuah believes that both elements of her latest research could improve the accuracy and usefulness of predictive disease models and, most importantly, patient health outcomes as well.

“In my work,” she says, “I’m always looking to solve problems that I know will have some kind of positive social impact.”

Read the full story at the Lehigh University News Center.

-Lori Friedman is a Director of Media Relations with Lehigh University's Office of Communications and Public Affairs.

July 20, 2017

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