Patient-level effectiveness prediction modeling for glioblastoma using classification trees
Publication typeJournal article with impact factor
JournalFrontiers in Pharmacology
Publication Begin page1
Publication End page10
MetadataShow full item record
AbstractLittle research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide.
KeywordReal-world Evidence (RWE), Oncology, Exploratory Study, Propensity Score Modeling, Decision Tree, Machine Learning
Knowledge Domain/IndustrySpecial Industries : Healthcare Management
- Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
- Authors: Anderson AB, Grazal CF, Balazs GC, Potter BK, Dickens JF, Forsberg JA
- Issue date: 2020 Jul
- Predicting Health Material Accessibility: Development of Machine Learning Algorithms.
- Authors: Ji M, Liu Y, Hao T
- Issue date: 2021 Sep 1
- Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach.
- Authors: Rodrigo H, Beukes EW, Andersson G, Manchaiah V
- Issue date: 2021 Nov 2
- Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.
- Authors: Jones S, Hargrave C, Deegan T, Holt T, Mengersen K
- Issue date: 2020 Apr