HomeProjectsAI for Early Detection of Sepsis & Infections
AI for Early Detection of Sepsis & Infections
People Involved
Dr. Unnikrishna Menon, Dr. Rahul Krishnan, Dr. Dipu T S, Dr. Merlin Moni
The Need Early detection of sepsis is a highly recognized need in healthcare to reduce mortality and improve patient outcomes.
Our Approach
Our team of researchers in collaboration with doctors at Amrita Institute of Medical Sciences is actively looking at ways to predict onset of sepsis ahead of time so as to save lives. We work on the following three major goals.
Use data driven techniques to understand the physiological presentations of sepsis and thereby use sensing devices generated data to more precisely identify early signs of sepsis
Impact of IoT connected remote patient monitoring system for patients with high risk of rapid deterioration and its integration to the rapid response team in preventing Out-of-ICU ward crashes
Development of explainable-AI tool for optimizing antibiotic prescriptions in patients with hematological malignancies
Associated Funders
ICMR 2021-12140 grant funds “An observational prospective study to assess the impact of Internet-of-Things connected remote patient monitoring system for patients with high risk of rapid deterioration and its integration to the rapid response team in preventing Out-of-ICU ward crashes”.
Ekanath Srihari Rangan, Rahul Krishnan Pathinarupothi, Kanwaljeet J S Anand, Michael P Snyder, Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning, JAMIA Open, Volume 5, Issue 4, December 2022, ooac080, https://doi.org/10.1093/jamiaopen/ooac080
Pathinarupothi, Rahul Krishnan, Dipu T. Sathyapalan, Merlin Moni, KA Unnikrishna Menon, and Maneesha Vinodini Ramesh. “REWOC: Remote Early Warning of Out-of-ICU Crashes in COVID Care Areas using IoT Device.” In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2010-2013. IEEE, 2021.
Shaji, S., Pathinarupothi, R.K., Rangan, E.S., Menon, K.U. and Ramesh, M.V., 2021, October. Heart Lung Health Monitor: Remote At-Home Patient Surveillance for Pandemic Management. In 2021 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 127-130). IEEE.
Tools
Vital-SEP is a Sepsis Prediction Engine that employs Gradient Boosted Decision Tree (XGBoost) on features extracted from vitals obtained from wearable sensors. We have open sourced our implementation as well as the models for the larger research community. https://pprahul.github.io/Vital-SEP/
