Machine learning approaches for predicting radiation therapy outcomes: A clinician's perspective

International Journal of Radiation Oncology Biology Physics

Volumn 93, Issue 5, 2015, Pages 1127-1135

  Kang, John ^a   Schwartz, Russell ^b   Flickinger, John ^c   Beriwal, Sushil ^d  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^d UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; CLINICAL RESEARCH; COMPLEX NETWORKS; FACE RECOGNITION; FORECASTING; LOGISTIC REGRESSION; ONCOLOGY; RADIATION EFFECTS; RADIOTHERAPY; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION;

APPLIED MACHINE LEARNING; FORMAL TRAINING; HANDWRITING RECOGNITION; MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MEDICAL MODELING; RADIATION ONCOLOGY; TARGETED ADVERTISING;

LEARNING SYSTEMS;

ARTIFICIAL NEURAL NETWORK; AUTOANALYSIS; BOOTSTRAPPING; CANCER PROGNOSIS; CANCER RADIOTHERAPY; CURVE FITTING; DATA ANALYSIS; DECISION TREE; DOSIMETRY; HUMAN; LINEAR SYSTEM; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REVIEW; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ARTIFICIAL INTELLIGENCE; ESOPHAGITIS; LUNG NEOPLASMS; ONCOLOGY; PROGNOSIS; RADIATION DOSE; RADIATION PNEUMONITIS; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME; TRENDS; XEROSTOMIA;

ARTIFICIAL INTELLIGENCE; ESOPHAGITIS; HUMANS; LOGISTIC MODELS; LUNG NEOPLASMS; MACHINE LEARNING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROGNOSIS; RADIATION ONCOLOGY; RADIATION PNEUMONITIS; RADIOTHERAPY; RADIOTHERAPY DOSAGE; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME; XEROSTOMIA;

EID: 84946716146     PISSN: 03603016     EISSN: 1879355X     Source Type: Journal    
DOI: 10.1016/j.ijrobp.2015.07.2286     Document Type: Review

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