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Eliane Birba

Student

Project Title

Machine Learning for Better Outcomes in Hematopathology Using Platelet-based Biomarkers and Mass Spectrometry Data

Project Description

The application of Artificial intelligence (AI) in healthcare has increased significantly in recent years, driven mainly by an abundance of data and powerful, accessible tools. Millions of blood cells are evaluated for hematological diagnostics by clinicians every day and this area offers significant opportunities for AI and machine learning. AI offers many promising tools to clinicians working in hematology to speed up and automate blood analysis. The proposed research seeks to employ AI in the analysis of hematopathology. The first objective is to develop a new algorithm for detection of Preeclamptic toxemia (PET) in pregnant women. Annually, PET claims the lives of 50,000 mothers and 500,000 babies and accurate, easily deployed detection tools do not exist. Therefore, there is an urgent, unmet challenge to develop accurate risk stratification tools for PET. This work will develop a new solution for risk stratification in PET. To achieve this, we will use a machine-learning algorithm to build a reliable and accurate test for preeclampsia, with a simple and easily interpretable score that can be deployed and implemented into widespread clinical use. This project will be a collaboration of the larger SFI-funded AI_PREMie project and take advantage of data collected and collaborator expertise within that project. The second objective is to apply machine learning to mass spectrometry (MS)-based proteomics analysis. This is an emerging area for application of machine learning techniques and there are a lot of research opportunities. Proteomics’ study is crucial for drug development, early diagnosis, and monitoring of diseases. One main challenge is that the proteome or the set of proteins in a cell/tissue/organism varies from time to time. Therefore, AI and machine learning methods can help fast and accurate protein pattern recognition and classification. This strand of the work will develop new techniques for the application of ML techniques to proteomics data, with a particular focus on making them accessible to clinicians