148 related articles for article (PubMed ID: 37428440)
1. Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning.
Gu X; Salehi A; Wang L; Coates PJ; Sgaramella N; Nylander K
J Oral Pathol Med; 2023 Aug; 52(7):637-643. PubMed ID: 37428440
[TBL] [Abstract][Full Text] [Related]
2. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.
Yi F; Yang H; Chen D; Qin Y; Han H; Cui J; Bai W; Ma Y; Zhang R; Yu H
BMC Med Inform Decis Mak; 2023 Jul; 23(1):137. PubMed ID: 37491248
[TBL] [Abstract][Full Text] [Related]
3. Prediction of 5-year overall survival of tongue cancer based machine learning.
Li L; Pu C; Jin N; Zhu L; Hu Y; Cascone P; Tao Y; Zhang H
BMC Oral Health; 2023 Aug; 23(1):567. PubMed ID: 37574562
[TBL] [Abstract][Full Text] [Related]
4. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
Liu L; Bi B; Cao L; Gui M; Ju F
Front Endocrinol (Lausanne); 2024; 15():1320335. PubMed ID: 38481447
[TBL] [Abstract][Full Text] [Related]
5. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
[TBL] [Abstract][Full Text] [Related]
6. Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.
Huang J; Chen H; Deng J; Liu X; Shu T; Yin C; Duan M; Fu L; Wang K; Zeng S
Front Neurol; 2023; 14():1185447. PubMed ID: 37614971
[TBL] [Abstract][Full Text] [Related]
7. Application of machine learning techniques for predicting survival in ovarian cancer.
Sorayaie Azar A; Babaei Rikan S; Naemi A; Bagherzadeh Mohasefi J; Pirnejad H; Bagherzadeh Mohasefi M; Wiil UK
BMC Med Inform Decis Mak; 2022 Dec; 22(1):345. PubMed ID: 36585641
[TBL] [Abstract][Full Text] [Related]
8. Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.
Ma M; Liu R; Wen C; Xu W; Xu Z; Wang S; Wu J; Pan D; Zheng B; Qin G; Chen W
Eur Radiol; 2022 Mar; 32(3):1652-1662. PubMed ID: 34647174
[TBL] [Abstract][Full Text] [Related]
9. A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.
Zhang G; Shi Y; Yin P; Liu F; Fang Y; Li X; Zhang Q; Zhang Z
Front Oncol; 2022; 12():944569. PubMed ID: 35957890
[TBL] [Abstract][Full Text] [Related]
10. Machine Learning to Predict the Response to Lenvatinib Combined with Transarterial Chemoembolization for Unresectable Hepatocellular Carcinoma.
Ma J; Bo Z; Zhao Z; Yang J; Yang Y; Li H; Yang Y; Wang J; Su Q; Wang J; Chen K; Yu Z; Wang Y; Chen G
Cancers (Basel); 2023 Jan; 15(3):. PubMed ID: 36765583
[TBL] [Abstract][Full Text] [Related]
11. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.
Ye Z; An S; Gao Y; Xie E; Zhao X; Guo Z; Li Y; Shen N; Ren J; Zheng J
Eur J Med Res; 2023 Jan; 28(1):33. PubMed ID: 36653875
[TBL] [Abstract][Full Text] [Related]
12. Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.
Xu Y; Sun X; Liu Y; Huang Y; Liang M; Sun R; Yin G; Song C; Ding Q; Du B; Bi X
Front Neurol; 2023; 14():1123607. PubMed ID: 37416313
[TBL] [Abstract][Full Text] [Related]
13. Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.
Chen Z; Wang Y; Ying MTC; Su Z
J Nephrol; 2024 Feb; ():. PubMed ID: 38315278
[TBL] [Abstract][Full Text] [Related]
14. Investigation on explainable machine learning models to predict chronic kidney diseases.
Ghosh SK; Khandoker AH
Sci Rep; 2024 Feb; 14(1):3687. PubMed ID: 38355876
[TBL] [Abstract][Full Text] [Related]
15. Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability.
Charlton CE; Poon MTC; Brennan PM; Fleuriot JD
Comput Methods Programs Biomed; 2023 May; 233():107482. PubMed ID: 36947980
[TBL] [Abstract][Full Text] [Related]
16. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
Li J; Liu S; Hu Y; Zhu L; Mao Y; Liu J
J Med Internet Res; 2022 Aug; 24(8):e38082. PubMed ID: 35943767
[TBL] [Abstract][Full Text] [Related]
17. A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy.
Pan X; Feng T; Liu C; Savjani RR; Chin RK; Sharon Qi X
J Cancer Res Clin Oncol; 2023 Aug; 149(10):6813-6825. PubMed ID: 36807760
[TBL] [Abstract][Full Text] [Related]
18. An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer.
Alabi RO; Almangush A; Elmusrati M; Leivo I; Mäkitie AA
Int J Med Inform; 2022 Dec; 168():104896. PubMed ID: 36279655
[TBL] [Abstract][Full Text] [Related]
19. Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.
Hu C; Li L; Huang W; Wu T; Xu Q; Liu J; Hu B
Infect Dis Ther; 2022 Jun; 11(3):1117-1132. PubMed ID: 35399146
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.
Luo XQ; Kang YX; Duan SB; Yan P; Song GB; Zhang NY; Yang SK; Li JX; Zhang H
J Med Internet Res; 2023 Jan; 25():e41142. PubMed ID: 36603200
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]