Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level
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Summary
Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level and its applications in Medicine and Urology.
- Type
- article
- Published
- 2019-08-30
- Cited by
- 9
- References
- 24
- Access
- Open access
- OpenAlex
- https://openalex.org/W2971216795
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:202800362
Keywords
Prostate cancer, Prostate-specific antigen, Medicine, Oncology, Artificial intelligence
References
- Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
- PSA density improves prediction of prostate cancer.
- PSA density: The comeback kid?
- Trends in the treatment of localized prostate cancer using supplemented cancer registry data
- The Prostate Health Index Selectively Identifies Clinically Significant Prostate Cancer
- Active Surveillance Versus Surgery for Low Risk Prostate Cancer: A Clinical Decision Analysis
- PSA density is superior than PSA and Gleason score for adverse pathologic features prediction in patients with clinically localized prostate cancer.
- Performance of the prostate cancer antigen 3 (PCA3) gene and prostate-specific antigen in prescreened men: exploring the value of PCA3 for a first-line diagnostic test.
- XGBoost: A Scalable Tree Boosting System
- Prostate cancer trends in Asia
- Evaluating the Four Kallikrein Panel of the 4Kscore for Prediction of High-grade Prostate Cancer in Men in the Canary Prostate Active Surveillance Study
- Management of Prostate Cancer in Elderly Patients: Recommendations of a Task Force of the International Society of Geriatric Oncology.
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree
- Prediction of prostate cancer by deep learning with multilayer artificial neural network
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods
- Accuracy and Variability of Prostate Multiparametric Magnetic Resonance Imaging Interpretation Using the Prostate Imaging Reporting and Data System: A Blinded Comparison of Radiologists.
- The Regression Analysis of Binary Sequences
- Random Forests
- Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images
Cited by
- Machine Learning Techniques in Prostate Cancer Diagnosis According to Prostate-Specific Antigen Levels and Prostate Cancer Gene 3 Score
- Prediction of Prostate Cancer using Machine Learning Algorithms
- Hospital-based prostate cancer screening in vietnamese men with lower urinary tract symptoms: a classification and regression tree model
- Prediction of The Gleason Group of Prostate Cancer from Clinical Biomarkers: Machine and Deep Learning from Tabular Data
- Prostate Cancer Diagnosis from Structured Clinical Biomarkers with Deep Learning: Anonymous Authors
- Prostate Cancer Diagnosis via Visual Representation of Tabular Data and Deep Transfer Learning
- Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach
- Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI
- AI applications in prevalent diseases and disorders
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