134 related articles for article (PubMed ID: 37130760)
1. Machine learning versus regression for prediction of sporadic pancreatic cancer.
Chen W; Zhou B; Jeon CY; Xie F; Lin YC; Butler RK; Zhou Y; Luong TQ; Lustigova E; Pisegna JR; Wu BU
Pancreatology; 2023 Jun; 23(4):396-402. PubMed ID: 37130760
[TBL] [Abstract][Full Text] [Related]
2. Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.
Chen W; Zhou Y; Xie F; Butler RK; Jeon CY; Luong TQ; Zhou B; Lin YC; Lustigova E; Pisegna JR; Kim S; Wu BU
Am J Gastroenterol; 2023 Jan; 118(1):157-167. PubMed ID: 36227806
[TBL] [Abstract][Full Text] [Related]
3. Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.
Tian J; Cui R; Song H; Zhao Y; Zhou T
Int Urol Nephrol; 2024 Jan; 56(1):237-247. PubMed ID: 37256426
[TBL] [Abstract][Full Text] [Related]
4. Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods.
Chen Q; Hu Y; Lin W; Huang Z; Li J; Lu H; Dai R; You L
Sci Rep; 2024 Mar; 14(1):5273. PubMed ID: 38438400
[TBL] [Abstract][Full Text] [Related]
5. Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.
Herrin J; Abraham NS; Yao X; Noseworthy PA; Inselman J; Shah ND; Ngufor C
JAMA Netw Open; 2021 May; 4(5):e2110703. PubMed ID: 34019087
[TBL] [Abstract][Full Text] [Related]
6. Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database.
Sun H; Wu S; Li S; Jiang X
Medicine (Baltimore); 2023 Mar; 102(10):e33144. PubMed ID: 36897699
[TBL] [Abstract][Full Text] [Related]
7. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.
Yang X; Qiu H; Wang L; Wang X
J Med Internet Res; 2023 Oct; 25():e44417. PubMed ID: 37883174
[TBL] [Abstract][Full Text] [Related]
8. Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.
Mukherjee S; Patra A; Khasawneh H; Korfiatis P; Rajamohan N; Suman G; Majumder S; Panda A; Johnson MP; Larson NB; Wright DE; Kline TL; Fletcher JG; Chari ST; Goenka AH
Gastroenterology; 2022 Nov; 163(5):1435-1446.e3. PubMed ID: 35788343
[TBL] [Abstract][Full Text] [Related]
9. [Application value of machine learning algorithms for predicting recurrence after resection of early-stage hepatocellular carcinoma].
Ji GW; Wang K; Xia YX; Li XC; Wang XH
Zhonghua Wai Ke Za Zhi; 2021 Aug; 59(8):679-685. PubMed ID: 34192861
[No Abstract] [Full Text] [Related]
10. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
Chen X; Pan J; Li Y; Tang R
Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
[TBL] [Abstract][Full Text] [Related]
11. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma.
Bian Y; Liu YF; Jiang H; Meng Y; Liu F; Cao K; Zhang H; Fang X; Li J; Yu J; Feng X; Li Q; Wang L; Lu J; Shao C
Abdom Radiol (NY); 2021 Oct; 46(10):4800-4816. PubMed ID: 34189612
[TBL] [Abstract][Full Text] [Related]
12. The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study.
Xiao J; Mo M; Wang Z; Zhou C; Shen J; Yuan J; He Y; Zheng Y
JMIR Med Inform; 2022 Feb; 10(2):e33440. PubMed ID: 35179504
[TBL] [Abstract][Full Text] [Related]
13. A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging.
Kaissis G; Ziegelmayer S; Lohöfer F; Algül H; Eiber M; Weichert W; Schmid R; Friess H; Rummeny E; Ankerst D; Siveke J; Braren R
Eur Radiol Exp; 2019 Oct; 3(1):41. PubMed ID: 31624935
[TBL] [Abstract][Full Text] [Related]
14. Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma：A population-based retrospective study.
Chen D; Liang S; Chen J; Li K; Mi H
Heliyon; 2024 Jan; 10(1):e23442. PubMed ID: 38163093
[TBL] [Abstract][Full Text] [Related]
15. [Efficacy of machine learning models
Gao K; Wang Y; Cao H; Jia J
Nan Fang Yi Ke Da Xue Xue Bao; 2023 Jun; 43(6):952-963. PubMed ID: 37439167
[TBL] [Abstract][Full Text] [Related]
16. Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models.
Xia K; Chen D; Jin S; Yi X; Luo L
Sci Rep; 2023 Sep; 13(1):14827. PubMed ID: 37684259
[TBL] [Abstract][Full Text] [Related]
17. A machine learning-based model for predicting distant metastasis in patients with rectal cancer.
Qiu B; Shen Z; Wu S; Qin X; Yang D; Wang Q
Front Oncol; 2023; 13():1235121. PubMed ID: 37655097
[TBL] [Abstract][Full Text] [Related]
18. A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning.
Chen SM; Phuc PT; Nguyen PA; Burton W; Lin SJ; Lin WC; Lu CY; Hsu MH; Cheng CT; Hsu JC
Cancer Med; 2023 Oct; 12(19):19987-19999. PubMed ID: 37737056
[TBL] [Abstract][Full Text] [Related]
19. Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.
Qiu B; Shen Z; Yang D; Wang Q
Front Oncol; 2023; 13():1183072. PubMed ID: 37293595
[TBL] [Abstract][Full Text] [Related]
20. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.
Rahimian F; Salimi-Khorshidi G; Payberah AH; Tran J; Ayala Solares R; Raimondi F; Nazarzadeh M; Canoy D; Rahimi K
PLoS Med; 2018 Nov; 15(11):e1002695. PubMed ID: 30458006
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
