Data Science for Health

Data science provides excellent tools for health professionals to gain valuable knowledge from patient information and dataset. At the D2K Lab, students have the opportunity to work on projects sponsored by medical centers, researchers and health solution companies to tackle various topics of the industry, such as ECGs, preventive modeling, imaging analytics, and diagnostic accuracy, etc. 

Impact Projects:

An Automated Early Warning System to Help Doctors Save Babies

Rice D2K Lab students working with Texas Children’s Hospital created machine-learning algorithms to increase detection time before cardiac arrest

Cardiac arrest events are often preceded by subtle, almost undetectable signs. What if doctors can detect early signals faster and more accurately with deep-learning algorithms?

Team Cardiac Signals, six senior electrical and computer engineering majors at Rice University, developed a metric to give an early warning for cardiac events by analyzing electrocardiogram (ECG) signals. “This metric can make life-saving treatments proactive instead of reactive,” said Frank Yang, a physics major.

Rice D2K Lab students working with Texas Children’s Hospital created machine-learning algorithms to increase detection time before cardiac arrest

Sponsored by Texas Children’s Hospital, the physiological data were collected from 44 pediatric cardiac patients. Each was diagnosed with hypoplastic left heart syndrome (HLHS), a condition in which babies are born with only half of their hearts developed. Patients’ survival depended on cardiologists’ early detection of abnormalities.

The student team used auto-encoders, non-linear deep-learning algorithms to monitor the ECG heartbeats before cardiac arrest. “We learned the patterns of healthy heartbeats and compared other heartbeats to assess how well they followed these patterns,” Damaraju said. “Then, we ultimately detected when heartbeats began to deviate from healthy behavior which indicated cardiac electrical stability.”

Read the Full Story about Team Cardiac Signals' Project >>


Project Title: Stroke Chatbot and Risk Prediction


Description: We developed an iOS and an Android app to chat daily with patients recently recovered from a stroke, collect vital information and predict the risk for a recurrent stroke.

Team Members: Lebing Chen, Weiqi Lu and Xinglin Wang

Sponsor: UTHealth
Sponsor Mentor: Xiaoqian Jiang and Sean Savitz
Faculty Mentor: Arko Barman


Project Title: UTHealth COVID-19 Chatbot


Description: A Natural Language Processing (NLP)-based conversational chatbot developed from scientific literature that is able to interact with patients to build personalized profiles and actively monitor symptoms and conditions.

Team Members: Weiqi Lu, Emmett Bertram, Alan Ji, Paul Gao, and Hannah Lei.

Sponsor: UTHealth
Sponsor Mentor: Xiaoqian Jiang
Faculty Mentor: Arko Barman

Beat-to-beat Classification of Unlabeled ECGs in Adult Populations

Team Members: Alvin Magee, Anthony Chen, Xinyue Cui, Nicole Tan

ECG machines collect hours of data on patient heart activity each day, and doctors often do not have time to analyze all the data. Our goal is to process all the data and tag abnormalities for doctors to review.

Sponsor: Medical Informatics Corp.

Sponsor Mentor: Raajen Patel, Craig Rusin, Jamie Waugh, Vicken Asadourian

D2K Fellow: Randall Balestriero

Pediatric Cardiac ICU Arrhythmias Detection

Team Members: Robert Chen, Yanwan Dai, Yerin Han, Anirudh Kuchibhatla, Mario Paciuc, and Xin Tan

We develop a novel semi-supervised classification algorithm that detects JET, a lethal heart condition, in real-time by analyzing morphologies and features of the electrocardiogram and central venous pressure signals. This algorithm eliminates alarm fatigue and saves more children who suffer from arrhythmia.

Sponsor: Texas Children’s Hospital

Sponsor Mentor: Dr. Parag Jain and Raajen Patel

D2K Fellow: Souptik Barua

Faculty Mentor: Dr. Craig Rusin

Inferring Genomic Signatures in Age-Related Macular Degeneration Across Different Stages

Team Members: Yu Wu, Shryans Goyal, Zishi Wang, Minjun Park, Zach Moxley

We predict the importance of certain genes that are responsible for AMD across different stages. Using those genes, we analyze gene networks to give better information to doctors to find an effective cure for AMD which currently does not exist.

Sponsor: Baylor College of Medicine

Sponsor Mentor: Dr. Rinki Ratnapriya

D2K Fellow: Emma Zohner


Interested in working with Rice students and faculty on a real-world data science project? Send us an email at