199 related articles for article (PubMed ID: 38613173)
61. Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.
Mao Y; Zhu Z; Pan S; Lin W; Liang J; Huang H; Li L; Wen J; Chen G
J Diabetes Investig; 2023 Feb; 14(2):309-320. PubMed ID: 36345236
[TBL] [Abstract][Full Text] [Related]
62. Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting.
Zhang Y; Li L; Li Y; Zeng Z
Adv Clin Exp Med; 2023 Feb; 32(2):185-194. PubMed ID: 36226692
[TBL] [Abstract][Full Text] [Related]
63. Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients.
Karabacak M; Schupper AJ; Carr MT; Bhimani AD; Steinberger J; Margetis K
Spine J; 2024 Jun; 24(6):1065-1076. PubMed ID: 38365005
[TBL] [Abstract][Full Text] [Related]
64. A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.
Xinyang S; Shuang Z; Tianci S; Xiangyu H; Yangyang W; Mengying D; Jingran Z; Feng Y
Magn Reson Imaging; 2024 Apr; 107():15-23. PubMed ID: 38181835
[TBL] [Abstract][Full Text] [Related]
65. Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.
Zakariaee SS; Naderi N; Ebrahimi M; Kazemi-Arpanahi H
Sci Rep; 2023 Jul; 13(1):11343. PubMed ID: 37443373
[TBL] [Abstract][Full Text] [Related]
66. Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.
Yun K; He T; Zhen S; Quan M; Yang X; Man D; Zhang S; Wang W; Han X
J Transl Med; 2023 May; 21(1):353. PubMed ID: 37246225
[TBL] [Abstract][Full Text] [Related]
67. Explainable Machine Learning Model to Prediction EGFR Mutation in Lung Cancer.
Yang R; Xiong X; Wang H; Li W
Front Oncol; 2022; 12():924144. PubMed ID: 35814445
[TBL] [Abstract][Full Text] [Related]
68. 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]
69. Can we explain machine learning-based prediction for rupture status assessments of intracranial aneurysms?
Mu N; Rezaeitaleshmahalleh M; Lyu Z; Wang M; Tang J; Strother CM; Gemmete JJ; Pandey AS; Jiang J
Biomed Phys Eng Express; 2023 Mar; 9(3):. PubMed ID: 36626819
[TBL] [Abstract][Full Text] [Related]
70. Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma.
Tan JY; Adeoye J; Thomson P; Sharma D; Ramamurthy P; Choi SW
Anticancer Res; 2022 Dec; 42(12):5859-5866. PubMed ID: 36456152
[TBL] [Abstract][Full Text] [Related]
71. Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms.
Liu L; Liu W; Jia Z; Li Y; Wu H; Qu S; Zhu J; Liu X; Xu C
Heliyon; 2023 Oct; 9(10):e20928. PubMed ID: 37928390
[TBL] [Abstract][Full Text] [Related]
72. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.
Xiao Y; Chen Y; Huang R; Jiang F; Zhou J; Yang T
BMC Med Res Methodol; 2024 Apr; 24(1):92. PubMed ID: 38643122
[TBL] [Abstract][Full Text] [Related]
73. A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.
Zhang J; Cui X; Yang C; Zhong D; Sun Y; Yue X; Lan G; Zhang L; Lu L; Yuan H
Cancer Med; 2023 Sep; 12(17):18306-18316. PubMed ID: 37609808
[TBL] [Abstract][Full Text] [Related]
74. A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.
Liang X; Li X; Li G; Wang B; Liu Y; Sun D; Liu L; Zhang R; Ji S; Yan W; Yu R; Gao Z; Liu X
Clin Cardiol; 2024 Apr; 47(4):e24264. PubMed ID: 38563389
[TBL] [Abstract][Full Text] [Related]
75. A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.
Wang Z; Xu C; Liu W; Zhang M; Zou J; Shao M; Feng X; Yang Q; Li W; Shi X; Zang G; Yin C
Front Endocrinol (Lausanne); 2022; 13():1083569. PubMed ID: 36686417
[TBL] [Abstract][Full Text] [Related]
76. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.
Hao N; Sun P; Zhao W; Li X
Ecotoxicol Environ Saf; 2023 Apr; 255():114806. PubMed ID: 36948010
[TBL] [Abstract][Full Text] [Related]
77. An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.
Gao Y; Li ZA; Zhai XY; Han L; Zhang P; Cheng SJ; Yue JY; Cui HK
Front Neurosci; 2023; 17():1323270. PubMed ID: 38260008
[TBL] [Abstract][Full Text] [Related]
78. Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.
Han Y; Wang S
Front Public Health; 2023; 11():1271595. PubMed ID: 38026309
[TBL] [Abstract][Full Text] [Related]
79. 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]
80. A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database.
Yang S; Cao L; Zhou Y; Hu C
J Multidiscip Healthc; 2023; 16():2625-2640. PubMed ID: 37701177
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
