Geisinger and IBM Data Science Elite team have come up with a sophisticated predictive model using data from the integrated health system’s electronic health record to detect sepsis risk.
Dr. Shravan Kethireddy from Geisinger has led a team of scientists to build a predictive model for sepsis mortality based on data from an actual, critical hospital setting. They have partnered with the IBM Data Science and Al Elite teams to assemble a six-person swat team to develop two projects and these are a model to predict sepsis mortality and a tool to keep the team on top of the latest sepsis research.
“For clinicians, making a sepsis diagnosis can be very difficult, as the symptoms overlap with many other common illnesses. If we can identify patients more quickly and more accurately, we can administer the right treatments early and increase the chances of a positive outcome,” said Dr. Donna Wolk, division director, Molecular and Microbial Diagnostic and Development at Geisinger.
The team has made use of the IBM Watson Studio open source tools to build a predictive model that would ingest clinical data from thousands of de-identified sepsis patients spanning a decade.
“Our experience using machine learning and data science has been very positive, and we see huge potential to continue its use in the medical field,” said Dr. Vida Abedi, Geisinger Staff Scientist, Department of Molecular and Functional Genomics. “We are well on our way to breaking new ground in clinical care for sepsis and achieving more positive outcomes for our patients.”
Geisinger with the help of this new model can develop more personalized clinical care plans for at-risk sepsis patients. Geisinger hopes to increase patient chances of recovery by paying attention to the key factors linked to sepsis deaths.
“It’s very important for me as a clinician and a research scientist to save patient lives using all the knowledge of the data and the clinical background,” said Dr. Hosam Farag, a bioinformatics scientist in Geisinger’s Diagnostic Medicine Institute. “Machine Learning can close the care gaps and optimize the treatment. That makes me passionate about how to save patient lives.”
Image Source: IBM