Patient-level effectiveness prediction modeling for glioblastoma using classification trees
Publication type
Journal article with impact factorPublication Year
2020Journal
Frontiers in PharmacologyPublication Volume
10Publication Issue
JanuaryPublication Begin page
1Publication End page
10
Metadata
Show full item recordAbstract
Little 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.Keyword
Real-world Evidence (RWE), Oncology, Exploratory Study, Propensity Score Modeling, Decision Tree, Machine LearningKnowledge Domain/Industry
Special Industries : Healthcare Managementae974a485f413a2113503eed53cd6c53
10.3389/fphar.2019.01665
Scopus Count
Collections
Related articles
- Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.
- Authors: Kim H, Lee S, Lee S, Hong S, Kang H, Kim N
- Issue date: 2019 Oct 16
- The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.
- Authors: Rendell K, Koprinska I, Kyme A, Ebker-White AA, Dinh MM
- Issue date: 2019 Jun
- Propensity score stratification using bootstrap aggregating classification trees analysis.
- Authors: Otok BW, Musa M, Purhadi, Yasmirullah SDP
- Issue date: 2020 Jul
- Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?
- Authors: Thio QCBS, Karhade AV, Ogink PT, Raskin KA, De Amorim Bernstein K, Lozano Calderon SA, Schwab JH
- Issue date: 2018 Oct