CBMI@UTHSC

Mentors: Student: Shayantan Banerjee

<p>In this project we intend to integrate publicly available -omic and clinical datasets using natural language processing techniques. Combining genomics data with physiologic read-outs may be effective in creating robust machine learning and data analysis pipeline. The example microarray gene expression data can be downloaded from GEO (<a href="https://www.synapse.org/#!Synapse:syn5612563" target="_blank">https://www.synapse.org/#!Synapse:syn5612563</a>) and physiologic data from eICU (<a href="https://eicu-crd.mit.edu/" target="_blank">https://eicu-crd.mit.edu/</a>). The idea is to map phenotypic terms to causal genes (for sepsis) and follow the SIRS timeline to form the integrated data set. After that robust machine learning models can be formed using the integrated data and compared with already existing models.</p>

Mentors: Student: Aditya Singh-2

<p>Machine Learning has the ability to gain information, process it and give a well-defined output to the end-user. Machine Learning algorithms can recognize patterns in behaviour and create their own logic. I will be applying Machine Learning algorithms and deep learning classification techniques to predict the onset of fever in patients. I plan to do the following over the summer. • Analyze the data and select the relevant physiological variables from the dataset. • Extract the features from the physiological variables. • Use different Machine Learning algorithms on the processed data. • Use deep learning techniques on the processed data.</p>

Mentors: Student: Ronet Swaminathan

<p>Sepsis is a potentially life-threatening condition caused by the body's response to an infection. The body normally releases chemicals into the bloodstream to fight an infection. Sepsis occurs when the body's response to these chemicals is out of balance, triggering changes that can damage multiple organ systems. Our main goal here is to train a deep learning model in python using all of its symptoms for the prediction of early onset of sepsis. Depending upon the values fed into the application, a doctor should get a good idea whether a person is susceptible to sepsis and get an early alert which can be critical for diagnosis. The application should be able to make these predictions using only a minimal set of streaming physiological data in real-time. During the course of this project, new deep learning methods, using temporal convolutional neural networks or quasi RNN, a model will be developed to identify markers that predict the onset of sepsis in patients admitted to the intensive care unit. We shall develop this application using the eICU database.</p>