262 related articles for article (PubMed ID: 34260593)
21. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.
Chen C; Yang D; Gao S; Zhang Y; Chen L; Wang B; Mo Z; Yang Y; Hei Z; Zhou S
Respir Res; 2021 Mar; 22(1):94. PubMed ID: 33789673
[TBL] [Abstract][Full Text] [Related]
22. Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.
Liu Q; Pang B; Li H; Zhang B; Liu Y; Lai L; Le W; Li J; Xia T; Zhang X; Ou C; Ma J; Li S; Guo X; Zhang S; Zhang Q; Jiang M; Zeng Q
J Thorac Dis; 2021 Feb; 13(2):1215-1229. PubMed ID: 33717594
[TBL] [Abstract][Full Text] [Related]
23. Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis.
Liu H; Wang X; Tang K; Peng E; Xia D; Chen Z
Transl Androl Urol; 2021 Feb; 10(2):710-723. PubMed ID: 33718073
[TBL] [Abstract][Full Text] [Related]
24. Machine learning for hospital readmission prediction in pediatric population.
Silva NCD; Albertini MK; Backes AR; Pena GDG
Comput Methods Programs Biomed; 2024 Feb; 244():107980. PubMed ID: 38134648
[TBL] [Abstract][Full Text] [Related]
25. Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.
Li C; Chen L; Chou C; Ngorsuraches S; Qian J
Cardiovasc Toxicol; 2022 Feb; 22(2):130-140. PubMed ID: 34792740
[TBL] [Abstract][Full Text] [Related]
26. Development and validation of machine learning models for nonalcoholic fatty liver disease.
Peng HY; Duan SJ; Pan L; Wang MY; Chen JL; Wang YC; Yao SK
Hepatobiliary Pancreat Dis Int; 2023 Dec; 22(6):615-621. PubMed ID: 37005147
[TBL] [Abstract][Full Text] [Related]
27. A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer.
Ding L; Wang K; Zhang C; Zhang Y; Wang K; Li W; Wang J
Front Public Health; 2022; 10():916513. PubMed ID: 35844840
[TBL] [Abstract][Full Text] [Related]
28. Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve.
Liu J; Wang C; Yan R; Lu Y; Bai J; Wang H; Li R
Arch Gynecol Obstet; 2022 Oct; 306(4):1015-1025. PubMed ID: 35171347
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Non-Contrasted CT Radiomics for SAH Prognosis Prediction.
Shan D; Wang J; Qi P; Lu J; Wang D
Bioengineering (Basel); 2023 Aug; 10(8):. PubMed ID: 37627852
[TBL] [Abstract][Full Text] [Related]
31. Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.
Qin L; Liang Z; Xie J; Ye G; Guan P; Huang Y; Li X
J Gastrointest Oncol; 2023 Feb; 14(1):220-232. PubMed ID: 36915444
[TBL] [Abstract][Full Text] [Related]
32. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
Xiong W; Zhang L; She K; Xu G; Bai S; Liu X
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov; 34(11):1188-1193. PubMed ID: 36567564
[TBL] [Abstract][Full Text] [Related]
33. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
[No Abstract] [Full Text] [Related]
34. A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.
Hu P; Liu Y; Li Y; Guo G; Su Z; Gao X; Chen J; Qi Y; Xu Y; Yan T; Ye L; Sun Q; Deng G; Zhang H; Chen Q
Front Neurol; 2022; 13():791547. PubMed ID: 35359648
[TBL] [Abstract][Full Text] [Related]
35. Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model.
Li X; Wang Y; Xu J
J Affect Disord; 2022 Oct; 314():341-348. PubMed ID: 35882300
[TBL] [Abstract][Full Text] [Related]
36. Machine learning models predicting undertriage in telephone triage.
Inokuchi R; Iwagami M; Sun Y; Sakamoto A; Tamiya N
Ann Med; 2022 Dec; 54(1):2990-2997. PubMed ID: 36286496
[TBL] [Abstract][Full Text] [Related]
37. Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches.
Chang CH; Lin CH; Liu CY; Huang CS; Chen SJ; Lin WC; Yang HT; Lane HY
J Psychopharmacol; 2021 Mar; 35(3):265-272. PubMed ID: 33586518
[TBL] [Abstract][Full Text] [Related]
38. Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis.
Jiang H; Deng W; Zhou J; Ren G; Cai X; Li S; Hu B; Li C; Shi Y; Zhang N; Zheng Y; Chen Y; Jiang Q; Zhou Y
Int J Parasitol; 2021 Oct; 51(11):959-965. PubMed ID: 33891933
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.
Hou N; Li M; He L; Xie B; Wang L; Zhang R; Yu Y; Sun X; Pan Z; Wang K
J Transl Med; 2020 Dec; 18(1):462. PubMed ID: 33287854
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
