132 related articles for article (PubMed ID: 36937438)
1. A comparison of machine learning models and Cox proportional hazards models regarding their ability to predict the risk of gastrointestinal cancer based on metabolic syndrome and its components.
Tran TT; Lee J; Gunathilake M; Kim J; Kim SY; Cho H; Kim J
Front Oncol; 2023; 13():1049787. PubMed ID: 36937438
[TBL] [Abstract][Full Text] [Related]
2. A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy.
Qiu X; Gao J; Yang J; Hu J; Hu W; Kong L; Lu JJ
Front Oncol; 2020; 10():551420. PubMed ID: 33194609
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Machine learning-based prediction of 1-year mortality for acute coronary syndrome
Hadanny A; Shouval R; Wu J; Gale CP; Unger R; Zahger D; Gottlieb S; Matetzky S; Goldenberg I; Beigel R; Iakobishvili Z
J Cardiol; 2022 Mar; 79(3):342-351. PubMed ID: 34857429
[TBL] [Abstract][Full Text] [Related]
5. ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study.
Hui M; Ma J; Yang H; Gao B; Wang F; Wang J; Lv J; Zhang L; Yang L; Zhao M
J Clin Med; 2023 Feb; 12(4):. PubMed ID: 36836039
[TBL] [Abstract][Full Text] [Related]
6. Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.
Farhadian M; Dehdar Karsidani S; Mozayanimonfared A; Mahjub H
BMC Cardiovasc Disord; 2021 Jan; 21(1):38. PubMed ID: 33461487
[TBL] [Abstract][Full Text] [Related]
7. Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.
Astley JR; Reilly JM; Robinson S; Wild JM; Hatton MQ; Tahir BA
Radiother Oncol; 2024 Apr; 193():110084. PubMed ID: 38244779
[TBL] [Abstract][Full Text] [Related]
8. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest.
Yang Y; Ma X; Wang Y; Ding X
Updates Surg; 2022 Feb; 74(1):355-365. PubMed ID: 34003477
[TBL] [Abstract][Full Text] [Related]
9. Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: a 10-year prospective cohort study.
Wei YX; Liu BP; Zhang J; Wang XT; Chu J; Jia CX
J Psychiatr Res; 2021 Dec; 144():217-224. PubMed ID: 34700209
[TBL] [Abstract][Full Text] [Related]
10. Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.
Liu W; Tang X; Cui T; Zhao H; Song G
Front Nutr; 2023; 10():1286654. PubMed ID: 38075230
[TBL] [Abstract][Full Text] [Related]
11. Contribution of macro- and micronutrients intake to gastrointestinal cancer mortality in the ONCONUT cohort: Classical vs. modern approaches.
Donghia R; Guerra V; Pesole PL; Liso M
Front Nutr; 2023; 10():1066749. PubMed ID: 36755992
[TBL] [Abstract][Full Text] [Related]
12. Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study.
Wang Y; Zhang L; Niu M; Li R; Tu R; Liu X; Hou J; Mao Z; Wang Z; Wang C
Front Public Health; 2021; 9():606711. PubMed ID: 33681127
[No Abstract] [Full Text] [Related]
13. 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]
14. Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea.
Kim J; Mun S; Lee S; Jeong K; Baek Y
BMC Public Health; 2022 Apr; 22(1):664. PubMed ID: 35387629
[TBL] [Abstract][Full Text] [Related]
15. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
16. Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer.
Fabritius MP; Seidensticker M; Rueckel J; Heinze C; Pech M; Paprottka KJ; Paprottka PM; Topalis J; Bender A; Ricke J; Mittermeier A; Ingrisch M
J Clin Med; 2021 Aug; 10(16):. PubMed ID: 34441964
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning-Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data.
Bao L; Wang YT; Zhuang JL; Liu AJ; Dong YJ; Chu B; Chen XH; Lu MQ; Shi L; Gao S; Fang LJ; Xiang QQ; Ding YH
Front Oncol; 2022; 12():922039. PubMed ID: 35865475
[TBL] [Abstract][Full Text] [Related]
18. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
Liu Y; Zhou S; Wei H; An S
BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
[TBL] [Abstract][Full Text] [Related]
19. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics.
Oei RW; Lyu Y; Ye L; Kong F; Du C; Zhai R; Xu T; Shen C; He X; Kong L; Hu C; Ying H
J Pers Med; 2021 Aug; 11(8):. PubMed ID: 34442430
[TBL] [Abstract][Full Text] [Related]
20. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.
Chowdhury MZI; Leung AA; Walker RL; Sikdar KC; O'Beirne M; Quan H; Turin TC
Sci Rep; 2023 Jan; 13(1):13. PubMed ID: 36593280
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]