Natural language processing and information retrieval techniques have long been explored in real-world applications such as healthcare. However, there are still many problems within the healthcare that are in need of text processing techniques to advance their current state. Clinical decision support for cancer patient is one of these applications. This project intends to extend on the recent tasks proposed by TREC Clinical Decision Support and TREC Precision Medicine (http://www.trec-cds.org/) to develop text search and processing techniques that extracts relevant information given a patients condition and history to the medical practitioners. This project will focus on extracting relevant to a patient given information such a clinical notes, disease or demographics background from the literature and clinical trials. The candidate will work on information extraction techniques, as well as search and information retrieval algorithms.
Students applying for this scholarship should have a background in computer science/engineering and be interested in one or more of the following:
1- Natural language processing especially information extraction or text summarisation,
2- Machine learning techniques, such as support vector machines, or neural networks
3- Search and Information retrieval techniques, such as different ranking models
The student is expected to have Python programming skills. Familiarity with Scikit-learn, PyTorch or Tensorflow and experience in running code in clusters is a plus.
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