Prediction of second stroke from data collected using standard stroke protocol

Problem Statement: Stroke is a condition in which the supply of blood to the brain is restricted or stopped. When a stroke patient arrives at the hospital, a standard protocol is followed to provide the medical assistance to the patient as well as to assess the affect of the stroke on the brain. Generally, a second stroke may follow which can be devastating for the patient. Hence, it is critical to predict the next stroke and provide care that can avoid the next stroke or at least minimize its affects.

Issues with Current Solutions: The standard protocol at the hospital involves blood and urine tests as well as neuroimaging using CT and MRI scans. These tests are used to assess the damage done by the first stroke. However, the prediction of the next stroke depends on the doctor's experience and is very subjective. In many cases, the patient who is sent home after treatment, suffers the second more devastating stroke at home which can result in permanent disability.

Challenges: The standard protocol at the hospitals result in generation of lot of data, for example, hundreds of images from CT and MRI scans, hundreds of enzymes from blood and urine tests etc. The challenge is to collectively analyze all of this data and find correlations that can predict the second stroke for the patient.

Solution: This research will develop objective prediction method for occurrence of the second stroke from the data collected at the hospital using the standard protocol for stroke assessment, treatment and management. The analysis and development will involve machine learning techniques that handle multimodality data involving images, signals, text, and numbers.

Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz