SBMI was proud to host its first Healthcare Machine Learning Hackathon earlier this month. More than twenty students from the Houston area participated in the 24-hour event that was held on SBMI’s campus on Sept. 24 and 25.
The first place winner, Rice University PhD student, Qiang Zhang, earned an $800 prize. Bishal Lamichhane, another Rice University PhD student, finished in second place and earned a $400 prize. Carroll Vance, a University of Houston Computer Science undergraduate student, earned third place recognition.
The Hackathon themed centered around Sudden Unexpected Death in Epilepsy (SUDEP) and finding ways to recognize the warning signs. SUDEP accounts for approximately 3,000 deaths in the United States each year. SUDEP leads to a shutdown in brain, cardiac, and breathing function and one potential method for determining SUDEP risk is the accurate identification of certain electroencephalographic (EEG) features after epileptic seizures. Hackathon participants were asked to develop algorithms to detect the end of postictal generalized EEG suppression (PGES) by identifying the onset of slow activity after a generalized tonic-clonic (GTC) seizure.
“The students are very talented and I was rather impressed with the caliber of coding skills they demonstrated,” noted Xiaoqian Jiang, PhD, SBMI associate professor and Hackathon co-organizer. “Two of the top four finishers yielded algorithms that were extremely close to our faculty-generated solution. Their overall performance was excellent and we look forward to the next Hackathon.”
published on 09/23/2019 at 11:30 a.m.