We develop Biomedical and Clinical informatics applications

CBMI@UTHSC provide the researchers, and clinicians with customized biomedical and clinical informatics applications to help manage complex translational and clinical research needs and gain insights into various diseases and conditions.

In the past, we have developed an open source software for online feature extraction and machine learning pipeline for real-time analysis of streaming physiological data. We have also built machine learning and deep learning models to predict fever, sickle cell disease, and sepsis in children and adults.

Through the Google Summer of Code (GSoC) program, we are specifically interested in creating open-source web application to identify the onset of abnormal conditions using the large-scale real-time sensor data at the bed-side.

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  • reactjs
  • python
  • flask
  • javascript
  • tensorflow


  • Science and Medicine
  • machine learning
  • deep learning
  • sepsis prediction
  • sepsis detection
  • web applications
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CBMI@UTHSC 2019 Projects

  • Ronet Swaminathan
    Deep learning model for sepsis prediction using high-frequency data
    Sepsis is a potentially life-threatening condition caused by the body's response to an infection. The body normally releases chemicals into the...
  • Aditya Singh-2
    Fever prediction model using high-frequency real-time sensor data
    Machine Learning has the ability to gain information, process it and give a well-defined output to the end-user. Machine Learning algorithms can...
  • Shayantan Banerjee
    Integrating genomics and high-frequency physiologic data for sepsis detection
    In this project we intend to integrate publicly available -omic and clinical datasets using natural language processing techniques. Combining...