162 related articles for article (PubMed ID: 33386157)
1. A novel machine-learning algorithm for predicting mortality risk after hip fracture surgery.
Li Y; Chen M; Lv H; Yin P; Zhang L; Tang P
Injury; 2021 Jun; 52(6):1487-1493. PubMed ID: 33386157
[TBL] [Abstract][Full Text] [Related]
2. A prediction model of elderly hip fracture mortality including preoperative red cell distribution width constructed based on the random survival forest (RSF) and Cox risk ratio regression.
Zhou YF; Wang J; Wang XL; Song SS; Bai Y; Li JL; Luo JY; Jin QQ; Cai WC; Yuan KM; Li J
Osteoporos Int; 2024 Apr; 35(4):613-623. PubMed ID: 38062161
[TBL] [Abstract][Full Text] [Related]
3. Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.
Kitcharanant N; Chotiyarnwong P; Tanphiriyakun T; Vanitcharoenkul E; Mahaisavariya C; Boonyaprapa W; Unnanuntana A
BMC Geriatr; 2022 May; 22(1):451. PubMed ID: 35610589
[TBL] [Abstract][Full Text] [Related]
4. Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis.
de Vries BCS; Hegeman JH; Nijmeijer W; Geerdink J; Seifert C; Groothuis-Oudshoorn CGM
Osteoporos Int; 2021 Mar; 32(3):437-449. PubMed ID: 33415373
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Predicting 30-day mortality following hip fracture surgery: evaluation of six risk prediction models.
Karres J; Heesakkers NA; Ultee JM; Vrouenraets BC
Injury; 2015 Feb; 46(2):371-7. PubMed ID: 25464983
[TBL] [Abstract][Full Text] [Related]
7. Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches.
Forssten MP; Bass GA; Ismail AM; Mohseni S; Cao Y
J Pers Med; 2021 Jul; 11(8):. PubMed ID: 34442370
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.
Guo J; He Q; Li Y
Ann Med; 2024 Dec; 56(1):2357225. PubMed ID: 38902847
[TBL] [Abstract][Full Text] [Related]
9. A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures.
Xing F; Luo R; Liu M; Zhou Z; Xiang Z; Duan X
Front Med (Lausanne); 2022; 9():829977. PubMed ID: 35646950
[TBL] [Abstract][Full Text] [Related]
10. Combination of red cell distribution width and American Society of Anesthesiologists score for hip fracture mortality prediction.
Yin P; Lv H; Zhang L; Long A; Zhang L; Tang P
Osteoporos Int; 2016 Jun; 27(6):2077-87. PubMed ID: 26975875
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.
Karnuta JM; Navarro SM; Haeberle HS; Billow DG; Krebs VE; Ramkumar PN
J Orthop Trauma; 2019 Jul; 33(7):324-330. PubMed ID: 30730360
[TBL] [Abstract][Full Text] [Related]
13. Artificial Neural Network and Cox Regression Models for Predicting Mortality after Hip Fracture Surgery: A Population-Based Comparison.
Chen CY; Chen YF; Chen HY; Hung CT; Shi HY
Medicina (Kaunas); 2020 May; 56(5):. PubMed ID: 32438724
[TBL] [Abstract][Full Text] [Related]
14. Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old.
Langsetmo L; Schousboe JT; Taylor BC; Cauley JA; Fink HA; Cawthon PM; Kado DM; Ensrud KE;
JBMR Plus; 2023 Aug; 7(8):e10757. PubMed ID: 37614297
[TBL] [Abstract][Full Text] [Related]
15. Hip fracture patients who experience a greater fluctuation in RDW during hospital course are at heightened risk for all-cause mortality: a prospective study with 2-year follow-up.
Yin P; Lv H; Li Y; Meng Y; Zhang L; Zhang L; Tang P
Osteoporos Int; 2018 Jul; 29(7):1559-1567. PubMed ID: 29656346
[TBL] [Abstract][Full Text] [Related]
16. Predicting Early Mortality After Hip Fracture Surgery: The Hip Fracture Estimator of Mortality Amsterdam.
Karres J; Kieviet N; Eerenberg JP; Vrouenraets BC
J Orthop Trauma; 2018 Jan; 32(1):27-33. PubMed ID: 28906306
[TBL] [Abstract][Full Text] [Related]
17. Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).
Zhang L; Huang T; Xu F; Li S; Zheng S; Lyu J; Yin H
BMC Emerg Med; 2022 Feb; 22(1):26. PubMed ID: 35148680
[TBL] [Abstract][Full Text] [Related]
18. Red Cell Distribution Width as an Independent Predictor of Long-Term Mortality in Hip Fracture Patients: A Prospective Cohort Study.
Lv H; Zhang L; Long A; Mao Z; Shen J; Yin P; Li M; Zeng C; Zhang L; Tang P
J Bone Miner Res; 2016 Jan; 31(1):223-33. PubMed ID: 26183903
[TBL] [Abstract][Full Text] [Related]
19. Predictive Value of Machine Learning Models in Postoperative Mortality of Older Adults Patients with Hip Fracture: A Systematic Review and Meta-analysis.
Liu F; Liu C; Tang X; Gong D; Zhu J; Zhang X
Arch Gerontol Geriatr; 2023 Dec; 115():105120. PubMed ID: 37473692
[TBL] [Abstract][Full Text] [Related]
20. Combination of measures of handgrip strength and red cell distribution width can predict in-hospital complications better than the ASA grade after hip fracture surgery in the elderly.
Ji HM; Han J; Bae HW; Won YY
BMC Musculoskelet Disord; 2017 Aug; 18(1):375. PubMed ID: 28854917
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]