143 related articles for article (PubMed ID: 37735621)
1. Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.
Alinia S; Asghari-Jafarabadi M; Mahmoudi L; Norouzi S; Safari M; Roshanaei G
Sci Rep; 2023 Sep; 13(1):15675. PubMed ID: 37735621
[TBL] [Abstract][Full Text] [Related]
2. Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.
Alinia S; Asghari-Jafarabadi M; Mahmoudi L; Roshanaei G; Safari M
Heliyon; 2024 Mar; 10(6):e27854. PubMed ID: 38515707
[TBL] [Abstract][Full Text] [Related]
3. [Establishment of artificial neural network model for predicting lymph node metastasis in patients with stage Ⅱ-Ⅲ gastric cancer].
Xue Z; Lu J; Lin J; Huang CM; Li P; Xie JW; Wang JB; Lin JX; Chen QY; Zheng CH
Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Apr; 25(4):327-335. PubMed ID: 35461201
[No Abstract] [Full Text] [Related]
4. Establishment and Reliability Evaluation of Prognostic Models in Diabetic Foot.
Chen YL; Zhu LP; Xu WC; Yang XP; Ji LQ; Chen QH; Lin CJ
Altern Ther Health Med; 2023 Nov; 29(8):534-539. PubMed ID: 37678850
[TBL] [Abstract][Full Text] [Related]
5. [Combined prognostic value of serum lactic acid, procalcitonin and severity score for short-term prognosis of septic shock patients].
Hao C; Hu Q; Zhu L; Xu H; Zhang Y
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Mar; 33(3):281-285. PubMed ID: 33834968
[TBL] [Abstract][Full Text] [Related]
6. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.
Keshtkar K; Safarpour AR; Heshmat R; Sotoudehmanesh R; Keshtkar A
Turk J Gastroenterol; 2023 Oct; 34(10):985-997. PubMed ID: 37681266
[TBL] [Abstract][Full Text] [Related]
7. Diagnostic test accuracy of nutritional tools used to identify undernutrition in patients with colorectal cancer: a systematic review.
Håkonsen SJ; Pedersen PU; Bath-Hextall F; Kirkpatrick P
JBI Database System Rev Implement Rep; 2015 May; 13(4):141-87. PubMed ID: 26447079
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of a predictive model for peripherally inserted central catheter-related thrombosis in breast cancer patients based on artificial neural network: A prospective cohort study.
Fu J; Cai W; Zeng B; He L; Bao L; Lin Z; Lin F; Hu W; Lin L; Huang H; Zheng S; Chen L; Zhou W; Lin Y; Fu F
Int J Nurs Stud; 2022 Nov; 135():104341. PubMed ID: 36084529
[TBL] [Abstract][Full Text] [Related]
9. Machine learning applications for the prediction of surgical site infection in neurological operations.
Tunthanathip T; Sae-Heng S; Oearsakul T; Sakarunchai I; Kaewborisutsakul A; Taweesomboonyat C
Neurosurg Focus; 2019 Aug; 47(2):E7. PubMed ID: 31370028
[TBL] [Abstract][Full Text] [Related]
10. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.
Fei Y; Hu J; Li WQ; Wang W; Zong GQ
J Thromb Haemost; 2017 Mar; 15(3):439-445. PubMed ID: 27960048
[TBL] [Abstract][Full Text] [Related]
11. Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network.
Porche K; Maciel CB; Lucke-Wold B; Robicsek SA; Chalouhi N; Brennan M; Busl KM
J Neurosurg Spine; 2022 Jan; 36(1):32-41. PubMed ID: 34507288
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.
Naseem M; Arshad H; Hashmi SA; Irfan F; Ahmed FS
Int J Med Inform; 2021 Oct; 154():104556. PubMed ID: 34455118
[TBL] [Abstract][Full Text] [Related]
14. Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks.
Korhani Kangi A; Bahrampour A
Asian Pac J Cancer Prev; 2018 Feb; 19(2):487-490. PubMed ID: 29480983
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. [Formulation of combined predictive indicators using logistic regression model in predicting sepsis and prognosis].
Duan L; Zhang S; Lin Z
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2017 Feb; 29(2):139-144. PubMed ID: 28625261
[TBL] [Abstract][Full Text] [Related]
17. Artificial neural networks improve the accuracy of cancer survival prediction.
Burke HB; Goodman PH; Rosen DB; Henson DE; Weinstein JN; Harrell FE; Marks JR; Winchester DP; Bostwick DG
Cancer; 1997 Feb; 79(4):857-62. PubMed ID: 9024725
[TBL] [Abstract][Full Text] [Related]
18. Factors influencing oncological outcomes in patients who develop pulmonary metastases after curative resection of colorectal cancer.
Kim CH; Huh JW; Kim HJ; Lim SW; Song SY; Kim HR; Na KJ; Kim YJ
Dis Colon Rectum; 2012 Apr; 55(4):459-64. PubMed ID: 22426271
[TBL] [Abstract][Full Text] [Related]
19. Artificial neural networks applied to survival prediction in breast cancer.
Lundin M; Lundin J; Burke HB; Toikkanen S; Pylkkänen L; Joensuu H
Oncology; 1999 Nov; 57(4):281-6. PubMed ID: 10575312
[TBL] [Abstract][Full Text] [Related]
20. Elevated tumor-to-liver uptake ratio (TLR) from
Huang J; Huang L; Zhou J; Duan Y; Zhang Z; Wang X; Huang P; Tan S; Hu P; Wang J; Huang M
Eur J Nucl Med Mol Imaging; 2017 Nov; 44(12):1958-1968. PubMed ID: 28812134
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]