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Artificial Intelligence in Personalized Healthcare

Machine learning and data-driven optimization can automate healthcare services in order to improve effectiveness, quality of care, and access to treatment. This research applies predictive and prescriptive modeling in personalized medicine (e.g., cancer therapy and otolaryngology) and clinical services (e.g., milk bank donation management).

Relevant publications

  1. Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review Amirhossein Moosavi, Steven Huang, Maryam Vahabi, Bahar Motamedivafa, Nelly Tian, Rafid Mahmood, Peter Liu, and Christopher LF Sun Journal of the American College of Cardiology: Advances 2024   DOI  
  2. Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization Timothy CY Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K Wong, and Ian Y Zhu Manufacturing & Service Operations Management 2023

    • First Place for the INFORMS Pierskalla Best Paper Award 2023.
      Finalist for the MSOM Practice-Based Research Competition 2023.
      Runner Up for the POMS College of Healthcare Operations Management (CHOM) Best Healthcare Paper Award 2023.
      Honorable Mention for the CORS Practice Prize Competition 2023.

    • Preliminary version appeared in The Journal of Nutrition.

  3. OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines Aaron Babier, Rafid Mahmood, Binghao Zhang, Victor GL Alves, Ana Maria Barragán-Montero, Joel Beaudry, Carlos E. Cardenas, Yankui Chang, Zijie Chen, Jaehee Chun, Kelly Diaz, Harold D Eraso, Erik Faustmann, Sibaji Gaj, Skylar Gay, Mary Gronberg, Bingqi Guo, Junjun He, Gerd Heilemann, Sanchit Hira, Yuliang Huang, Fuxin Ji, Dashan Jiang, Jean CJ Giraldo, Hoyeon Lee, Jun Lian, Shuolin Liu, Keng-Chi Liu, José Marrugo, Kentaro Miki, Kunio Nakamura, Tucker Netherton, Dan Nguyen, Hamidreza Nourzadeh, Alexander FI Osman, Zhao Peng, José Darío Quinto Muñoz, Christian Ramsl, Dong J Rhee, Juan D Rodriguez, Hongming Shan, Jeffrey V Siebers, Mumtaz H Soomro, Kay Sun, Andrés Usuga Hoyos, Carlos Valderrama, Rob Verbeek, Enpei Wang, Siri Willems, Qi Wu, Xuanang Xu, Sen Yang, Lulin Yuan, Simeng Zhu, Lukas Zimmermann, Kevin L Moore, Thomas G Purdie, Andrea L McNiven, and Timothy CY Chan Physics in Medicine and Biology 2022   arXiv  
  4. Prediction of protein and fat content in human donor milk using machine learning Rachel K Wong, Michael A Pitino, Rafid Mahmood, Ian Y Zhu, Deborah Stone, Sharon Unger, Deborah O’Connor, and Timothy CY Chan Journal of Nutrition 2021   DOI  
  5. OpenKBP: The open access knowledge-based planning grand challenge Aaron Babier, Binghao Zhang, Rafid Mahmood, Kevin Moore, Thomas G Purdie, Andrea McNiven, and Timothy CY Chan Medical Physics 2021   arXiv     Code     DOI  
  6. AutoAudio: Deep learning for automatic audiogram interpretation Matthew G Crowson, Jong W Lee, Amr Hamour, Rafid Mahmood, Aaron Babier, Vincent Lin, Debara L Tucci, and Timothy CY Chan Journal of Medical Systems 2020   DOI  
  7. Predicting post-operative cochlear implant performance using supervised machine learning Matthew G Crowson, Peter Dixon, Rafid Mahmood, Jong W Lee, David Shipp, Trung Le, Joseph Chen, and Timothy CY Chan Otology and Neurotology 2020   DOI  
  8. The importance of evaluating the complete automated knowledge-based planning pipeline Aaron Babier, Rafid Mahmood, Andrea L McNiven, Adam Diamant, and Timothy CY Chan Physica Medica: European Journal of Medical Physics 2020   arXiv     DOI  
  9. Knowledge-based automated planning with three-dimensional generative adversarial networks Aaron Babier, Rafid Mahmood, Andrea L McNiven, Adam Diamant, and Timothy CY Chan Medical Physics 2020   arXiv     DOI  

    • Preliminary version appeared in NeurIPS ML4H Workshop 2018.

  10. Automated treatment planning in radiation therapy using 3-D generative adversarial networks Aaron Babier, Rafid Mahmood, Andrea McNiven, Adam Diamant, and Timothy CY Chan NeurIPS Machine Learning for Health Workshop 2018 2018   Code     Paper  
  11. Automated treatment planning in radiation therapy using generative adversarial networks Rafid Mahmood, Aaron Babier, Andrea McNiven, Adam Diamant, and Timothy CY Chan Machine Learning for Healthcare Conference 2018   Code     Project Page