126 related articles for article (PubMed ID: 29677931)
1. Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning.
Gründner J; Prokosch HU; Stürzl M; Croner R; Christoph J; Toddenroth D
Stud Health Technol Inform; 2018; 247():101-105. PubMed ID: 29677931
[TBL] [Abstract][Full Text] [Related]
2. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.
Shi M; He J
Mol Biosyst; 2016 Apr; 12(4):1214-23. PubMed ID: 26864276
[TBL] [Abstract][Full Text] [Related]
3. Machine learning models in breast cancer survival prediction.
Montazeri M; Montazeri M; Montazeri M; Beigzadeh A
Technol Health Care; 2016; 24(1):31-42. PubMed ID: 26409558
[TBL] [Abstract][Full Text] [Related]
4. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK
Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719
[TBL] [Abstract][Full Text] [Related]
5. Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods.
Yahya N; Ebert MA; Bulsara M; House MJ; Kennedy A; Joseph DJ; Denham JW
Med Phys; 2016 May; 43(5):2040. PubMed ID: 27147316
[TBL] [Abstract][Full Text] [Related]
6. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.
López-García G; Jerez JM; Franco L; Veredas FJ
PLoS One; 2020; 15(3):e0230536. PubMed ID: 32214348
[TBL] [Abstract][Full Text] [Related]
7. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions.
Bottaci L; Drew PJ; Hartley JE; Hadfield MB; Farouk R; Lee PW; Macintyre IM; Duthie GS; Monson JR
Lancet; 1997 Aug; 350(9076):469-72. PubMed ID: 9274582
[TBL] [Abstract][Full Text] [Related]
8. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.
Zhang B; Liang XL; Gao HY; Ye LS; Wang YG
Genet Mol Res; 2016 May; 15(2):. PubMed ID: 27323037
[TBL] [Abstract][Full Text] [Related]
9. Predicting 5-year survival of colorectal carcinoma patients using data mining methods.
Chhieng D; Hardin M; Anderson B; Manne U
AMIA Annu Symp Proc; 2007 Oct; ():907. PubMed ID: 18694007
[TBL] [Abstract][Full Text] [Related]
10. A machine learning-based approach to prognostic analysis of thoracic transplantations.
Delen D; Oztekin A; Kong ZJ
Artif Intell Med; 2010 May; 49(1):33-42. PubMed ID: 20153956
[TBL] [Abstract][Full Text] [Related]
11. Neural networks in the prediction of survival in patients with colorectal cancer.
Grumett S; Snow P; Kerr D
Clin Colorectal Cancer; 2003 Feb; 2(4):239-44. PubMed ID: 12620144
[TBL] [Abstract][Full Text] [Related]
12. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.
Sakr S; Elshawi R; Ahmed AM; Qureshi WT; Brawner CA; Keteyian SJ; Blaha MJ; Al-Mallah MH
BMC Med Inform Decis Mak; 2017 Dec; 17(1):174. PubMed ID: 29258510
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of three machine learning models for self-referral decision support on low back pain in primary care.
Oude Nijeweme-d'Hollosy W; van Velsen L; Poel M; Groothuis-Oudshoorn CGM; Soer R; Hermens H
Int J Med Inform; 2018 Feb; 110():31-41. PubMed ID: 29331253
[TBL] [Abstract][Full Text] [Related]
14. Artificial neural networks--a method for prediction of survival following liver resection for colorectal cancer metastases.
Spelt L; Nilsson J; Andersson R; Andersson B
Eur J Surg Oncol; 2013 Jun; 39(6):648-54. PubMed ID: 23514791
[TBL] [Abstract][Full Text] [Related]
15. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.
Huang Y; Li J; Li M; Aparasu RR
BMC Med Res Methodol; 2023 Nov; 23(1):268. PubMed ID: 37957593
[TBL] [Abstract][Full Text] [Related]
16. Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.
Zeng Q; Liu Y; Zhao H; Sun M; Li X
Environ Pollut; 2017 Apr; 223():676-684. PubMed ID: 28196722
[TBL] [Abstract][Full Text] [Related]
17. Predicting cancer outcomes from histology and genomics using convolutional networks.
Mobadersany P; Yousefi S; Amgad M; Gutman DA; Barnholtz-Sloan JS; Velázquez Vega JE; Brat DJ; Cooper LAD
Proc Natl Acad Sci U S A; 2018 Mar; 115(13):E2970-E2979. PubMed ID: 29531073
[TBL] [Abstract][Full Text] [Related]
18. Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models.
Nwaokorie A; Fey D
Int J Mol Sci; 2021 Sep; 22(18):. PubMed ID: 34576133
[TBL] [Abstract][Full Text] [Related]
19. Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients.
Yang C; Zhou S; Zhu J; Sheng H; Mao W; Fu Z; Chen Z
Clin Chim Acta; 2022 Nov; 536():191-199. PubMed ID: 36191612
[TBL] [Abstract][Full Text] [Related]
20. Cleansing and Imputation of Body Mass Index Data and Its Impact on a Machine Learning Based Prediction Model.
Jauk S; Kramer D; Leodolter W
Stud Health Technol Inform; 2018; 248():116-123. PubMed ID: 29726427
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
