Type of pre-existing chronic conditions and their associations with Merkel cell carcinoma (MCC) treatment: Prediction and interpretation using machine learning methods.

Abstract

Authors

• Yves Paul Vincent Mbous
  Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown, West Virginia, USA.
• Zasim Azhar Siddiqui
  Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown.
• Murtuza Bharmal
  AstraZeneca, Oncology, Outcomes Research, Boston, Massachusetts, United States of America.
• Traci LeMasters
  Department of Pharmaceutical Systems and Policy, West Virginia University School of Pharmacy, Robert C. Byrd Health Sciences Center [North], P.O. Box 9510, Morgantown, WV, 26506-9510, USA. Electronic address: traci.lemasters@hsc.wvu.edu.
• Joanna Kolodney
  Department of Hematology/Oncology, School of Medicine, West Virginia University, Morgantown, West Virginia, United States of America.
• George A Kelley
  Department of Epidemiology and Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia, United States of America.
• Khalid Kamal
  Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown, West Virginia, USA.
• Usha Sambamoorthi
  Department of Pharmacotherapy, College of Pharmacy, "Vashisht" Professor of Disparities, Health Education, Awareness & Research in Disparities (HEARD) Scholar, Texas Center for Health Disparities, University of North Texas Health Sciences Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA. Electronic address: usha.sambamoorthi@unthsc.edu.

Keywords

External Resources