Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer

Biometrics

Volumn 67, Issue 4, 2011, Pages 1422-1433

  Zhao, Yufan ^a   Zeng, Donglin ^b   Socinski, Mark A ^c   Kosorok, Michael R ^b  

^a AMGEN INC   (United States)

^b University of North Carolina at Chapel Hill   (United States)

^c UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

Adaptive design; Dynamic treatment regime; Individualized therapy; Multistage decision problems; Nonsmall cell lung cancer; Personalized medicine; Q learning; Reinforcement learning; Support vector regression

Indexed keywords

BIOLOGICAL ORGANS; DISEASES; HOSPITAL DATA PROCESSING; INDIUM COMPOUNDS; MEDICAL APPLICATIONS; PATIENT TREATMENT;

ADAPTIVE DESIGNS; CLINICAL TRIAL; DYNAMIC TREATMENT REGIME; DYNAMIC TREATMENTS; MULTISTAGE DECISION PROBLEMS; NON SMALL CELL LUNG CANCER; PERSONALIZED MEDICINES; Q-LEARNING; REINFORCEMENT LEARNINGS; SUPPORT VECTOR REGRESSIONS;

REINFORCEMENT LEARNING;

CANCER; CELL ORGANELLE; DATA SET; DISEASE TREATMENT; NUMERICAL MODEL; REGRESSION ANALYSIS; RESPIRATORY DISEASE;

EID: 83655181241     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01572.x     Document Type: Article

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