Abstract
Predicting hematologic adverse events for ovarian cancer patients on platinum-based chemotherapy with machine learning survival models
Gynecologic oncology, v 208(supplement), pp S380-S380
May 2026
Abstract
To test the ability of machine learning survival models to predict time to hematologic adverse events (AEs) for ovarian cancer patients undergoing platinum-based chemotherapy. To determine the significant predictive factors known at the initiation of treatment.
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Details
- Title
- Predicting hematologic adverse events for ovarian cancer patients on platinum-based chemotherapy with machine learning survival models
- Creators
- John Nakayama - Allegheny Health NetworkTiffany Summerscales - Highmark Blue Cross Blue ShieldMike McGaughey - Highmark Health, Pittsburgh, PA, United StatesEirwen Miller - Allegheny Health NetworkThomas Krivak - UPMC Hillman Cancer CenterSarah Crafton - Allegheny Health NetworkChristopher Morse - Harvard University PressAlyssa Wield - Allegheny Health NetworkGrace Pindzola - Drexel University, Philadelphia, NY, United StatesSabrina Ortiz - Drexel University, College of MedicineTeresa Hong - Drexel University, College of MedicineJeffrey Toole - Highmark Blue Cross Blue Shield
- Publication Details
- Gynecologic oncology, v 208(supplement), pp S380-S380
- Conference
- Society of Gynecologic Oncology (SGO) 2026 Annual Meeting on Women's Cancer (San Juan, Puerto Rico, 10 Apr 2026–13 Apr 2026)
- Publisher
- Elsevier Inc
- Number of pages
- 1
- Resource Type
- Abstract
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001760912500035
- Other Identifier
- 991022189174104721