Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.
about
Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data OnlyProbabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.Development of a clinical decision model for thyroid nodulesContent-based image retrieval in radiology: current status and future directions.Development of a Bayesian classifier for breast cancer risk stratification: a feasibility study.Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer predictionBreast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.A molecular computational model improves the preoperative diagnosis of thyroid nodules.Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors.Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features.Screening in the dark: ethical considerations of providing screening tests to individuals when evidence is insufficient to support screening populationsProbability machines: consistent probability estimation using nonparametric learning machinesA network model to predict the risk of death in sickle cell disease.Decision support systems for clinical radiological practice -- towards the next generationComparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy.A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis.A probabilistic analysis of completely excised high-grade soft tissue sarcomas of the extremity: an application of a Bayesian belief network.Application of multivariate probabilistic (Bayesian) networks to substance use disorder risk stratification and cost estimation.Improving diagnostic recognition of primary hyperparathyroidism with machine learning.Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.Using automatically extracted information from mammography reports for decision-support.Machine learning to identify multigland disease in primary hyperparathyroidism.Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model.Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.A computational model for genetic and epigenetic signals in colon cancer.Understanding the complex relationships underlying hot flashes: a Bayesian network approach.A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists.
P2860
Q30984936-AD3FEEE7-9EBF-4726-AF20-6750F3F5C9B0Q33430221-7A0CC183-94DC-432A-B734-CA695C7F6EBEQ33491866-724D1314-0D8D-4F4F-85E2-9EB218A0DD58Q33550859-0A6BED93-C94E-45A9-B07F-9275568851A1Q33775221-20DF3AA0-E86D-4C58-97A8-1AB742106E82Q33923020-FF145AC2-F64D-4290-AA03-BC816215DBF4Q34060566-ACEF7F11-41E7-414D-8278-DCE9CE77B94DQ34408933-4994A3A8-7BFC-4AB6-ABC1-9610A96C7B73Q34670103-0979E84B-CB43-4E60-A800-BE50399C5595Q34866372-AAB71608-CFA0-4159-BF21-CCFBA6FC8AA2Q35046772-143CC7A4-3D72-432D-8EEF-EE6E2E3AA2A0Q35649137-483184AB-E5A9-453D-9F5C-7CD62AFAB1FFQ36007731-2259FA12-E641-4C11-96C3-C82A9003FCB0Q36324860-DB1FCCEB-B603-44A5-A7D6-AA80550DE55AQ36387040-E34BDD80-9469-4C3E-BF98-AD8A585FDE07Q37140708-4F06DAE5-7781-464D-95E7-3321B5B1DFFAQ37542656-CBDD377A-2045-4CF9-878A-67065E780731Q37603910-3641F848-2376-400C-897F-5776F70F9FC1Q37695070-66104BB0-09DC-4163-A476-F6D9783FD3BEQ39090824-5EDBB74B-7F9A-465D-86DD-EA6D8C6AC1E5Q39146414-064216CD-DF7D-4396-87EE-89EDA823DAE5Q39623472-28DBEBAB-B478-445A-9813-6E4765C624DAQ42602573-E052BE95-79F5-4821-ADEB-791F7E2FEA2FQ43652220-6A49FC4F-762F-4D5F-AD9A-0B8350C059BEQ45947181-2338A491-AA54-4180-812F-E02D54A3FE82Q46899948-05E94465-7146-4B22-B894-1BAD4591AFB5Q47680440-15BE68C1-D8EF-451A-8F6E-C7915AEB0D6BQ47933512-FAF6671A-A431-4179-9709-CB45562B5634
P2860
Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.
description
2006 nî lūn-bûn
@nan
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
2006年论文
@zh
2006年论文
@zh-cn
name
Bayesian network to predict br ...... y results: initial experience.
@ast
Bayesian network to predict br ...... y results: initial experience.
@en
type
label
Bayesian network to predict br ...... y results: initial experience.
@ast
Bayesian network to predict br ...... y results: initial experience.
@en
prefLabel
Bayesian network to predict br ...... y results: initial experience.
@ast
Bayesian network to predict br ...... y results: initial experience.
@en
P2093
P1433
P1476
Bayesian network to predict br ...... y results: initial experience.
@en
P2093
Daniel L Rubin
Gale A Sisney
Jason P Fine
Ross D Shachter
Winifred K Leung
P304
P356
10.1148/RADIOL.2403051096
P407
P577
2006-09-01T00:00:00Z