Prediction of survival in patients with esophageal carcinoma using artificial neural networks.
about
Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and ChallengesDevelopment and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residentsApplication of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.Application and Exploration of Big Data Mining in Clinical Medicine.Three-tiered risk stratification model to predict progression in Barrett's esophagus using epigenetic and clinical features.Proposed follow up programme after curative resection for lower third oesophageal cancer.A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapyImproving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review.Applications of machine learning in cancer prediction and prognosisSupport vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma.ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci.Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area.Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.Machine learning approaches to the social determinants of health in the health and retirement study.Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.Transcriptional factor Prox1 plays an essential role in the antiproliferative action of interferon-γ in esophageal cancer cells.
P2860
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P2860
Prediction of survival in patients with esophageal carcinoma using artificial neural networks.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
2005年论文
@zh
2005年论文
@zh-cn
name
Prediction of survival in pati ...... ng artificial neural networks.
@ast
Prediction of survival in pati ...... ng artificial neural networks.
@en
type
label
Prediction of survival in pati ...... ng artificial neural networks.
@ast
Prediction of survival in pati ...... ng artificial neural networks.
@en
prefLabel
Prediction of survival in pati ...... ng artificial neural networks.
@ast
Prediction of survival in pati ...... ng artificial neural networks.
@en
P2093
P2860
P356
P1433
P1476
Prediction of survival in pati ...... ng artificial neural networks.
@en
P2093
David Shibata
Florin M Selaru
Fumiaki Sato
Go Watanabe
Masato Maeda
Masayuki Imamura
Sanford A Stass
Stephen J Meltzer
Yuriko Mori
Yutaka Shimada
P2860
P304
P356
10.1002/CNCR.20938
P407
P577
2005-04-01T00:00:00Z