Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study

Annals of the New York Academy of Sciences

Volumn 1387, Issue 1, 2017, Pages 84-94

  Li, Qiongge ^a,b   Chan, Maria F ^c  

^a CITY UNIVERSITY OF NEW YORK   (United States)

^b CITY COLLEGE OF NEW YORK   (United States)

^c MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

ANNs; ARMA; artificial neural networks; autoregressive moving average; Linac QA; predictive time series analytics; radiotherapy

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER PATIENT; CANCER RADIOTHERAPY; CANCER THERAPY; COMPARATIVE STUDY; DOSIMETRY; EMPIRICISM; HEALTH CARE QUALITY; HUMAN; LINAC BEAM SYMMETRY; LINEAR ACCELERATOR; MACHINE LEARNING; PATIENT SAFETY; PREDICTION; QUALITY CONTROL; TIME SERIES ANALYSIS; ADVERSE EFFECTS; BAYES THEOREM; BIOLOGICAL MODEL; BIOLOGY; DEVICES; EMPIRICAL RESEARCH; FACTUAL DATABASE; FORECASTING; IN VIVO DOSIMETRY; KINETICS; LONGITUDINAL STUDY; NEOPLASMS; PROCEDURES; RADIOSURGERY; REGRESSION ANALYSIS; REPRODUCIBILITY; VALIDATION STUDY;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; EMPIRICAL RESEARCH; FORECASTING; HUMANS; IN VIVO DOSIMETRY; KINETICS; LONGITUDINAL STUDIES; MACHINE LEARNING; MODELS, BIOLOGICAL; NEOPLASMS; NEURAL NETWORKS (COMPUTER); QUALITY ASSURANCE, HEALTH CARE; RADIOSURGERY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 84987711717     PISSN: 00778923     EISSN: 17496632     Source Type: Journal    
DOI: 10.1111/nyas.13215     Document Type: Article

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