MSc, Physics, University of Lethbridge (2016)
PhD, Medical Physics, McGill University (in progress)
Dr. John Kildea
Bioinformatics, precision medicine in oncology
The majority of the cancer patients suffer from severe pain at some stage of their illness. In most cases, cancer pain is underestimated by clinical staff and is not properly managed until it reaches a severe stage. This inadequate management of pain affects cancer patients both physically, and mentally. Detecting cancer pain in its early stage is a challenging task but it could save cancer patients from suffering from severe distress. The objective of this research project is to detect pain at an early stage by analyzing patients’ medical images. Development of an algorithm to do this can be achieved by combining two computer science techniques: one that allows us to gather information about pain from medical notes, and one that extracts information from medical images. We will use the first technique to extract and quantify pain intensity recorded in patients' medical notes. The second technique will be employed to analyze radiographic images of cancer patients to extract information about their tumors (such as tumor volume and shape). Then, we will implement mathematical techniques to model the relationship between identified tumor features and extracted pain intensities. Finally, we will use pain scores that are directly collected from thousands of future cancer patients via a mobile app that has been developed in our group (opalmedapps.com) to validate our model. A significant and novel application of our model will be to predict pain using the radiographic images of cancer patients before they experience it. This will help to improve the quality of life of cancer patients.
H. Naseri†, K. Kafi, S. Skamene, M. Tolba, M.D. Faye, P. Ramia, J. Khriguian, J. Kildea, Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases, J. Biomed. Inform. 2021 Aug;120:103864. doi: 10.1016/j.jbi.2021.103864. Epub 2021 Jul 12.
Ruth & Alex Dworkin Scholarship, Faculty of Medicine (2020-2021)
Mitacs Research Training Award (2019)
RI-MUHC Studentship (2019-2020)
McGill Graduate Excellence Fellowship (2018)
H. Naseri†*, S. Skamene, M. Tolba, M. Faye, P. Ramia, J. Khriguian, H. Patrick†, A. Hernández†, J. Kildea, A simulation CT-based radiomics model for detecting metastatic spinal bone lesions, 63rd Annual Meeting & Exhibition of the American Association of Physicists in Medicine (AAPM), Virtual Meeting, July 25-29, 2021. Oral presentation.