168 related articles for article (PubMed ID: 38285801)
21. Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.
Agyekum EA; Wang YG; Xu FJ; Akortia D; Ren YZ; Chambers KH; Wang X; Taupa JO; Qian XQ
Sci Rep; 2023 Aug; 13(1):12604. PubMed ID: 37537230
[TBL] [Abstract][Full Text] [Related]
22. Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study.
Lu T; Fang Y; Liu H; Chen C; Li T; Lu M; Song D
Technol Cancer Res Treat; 2024; 23():15330338231222331. PubMed ID: 38190617
[TBL] [Abstract][Full Text] [Related]
23. Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.
Yuan Y; Ren J; Tao X
Eur Radiol; 2021 Sep; 31(9):6429-6437. PubMed ID: 33569617
[TBL] [Abstract][Full Text] [Related]
24. Prediction of postoperative infectious complications in elderly patients with colorectal cancer: a study based on improved machine learning.
Tian Y; Li R; Wang G; Xu K; Li H; He L
BMC Med Inform Decis Mak; 2024 Jan; 24(1):11. PubMed ID: 38184556
[TBL] [Abstract][Full Text] [Related]
25. Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms.
Zhang X; Feng C; Bai X; Peng X; Guo Q; Chen L; Xue J
Arch Esp Urol; 2023 Sep; 76(7):494-503. PubMed ID: 37867334
[TBL] [Abstract][Full Text] [Related]
26. Machine learning-based models for the prediction of breast cancer recurrence risk.
Zuo D; Yang L; Jin Y; Qi H; Liu Y; Ren L
BMC Med Inform Decis Mak; 2023 Nov; 23(1):276. PubMed ID: 38031071
[TBL] [Abstract][Full Text] [Related]
27. Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods.
Taghizadeh E; Heydarheydari S; Saberi A; JafarpoorNesheli S; Rezaeijo SM
BMC Bioinformatics; 2022 Oct; 23(1):410. PubMed ID: 36183055
[TBL] [Abstract][Full Text] [Related]
28. Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.
Kocak B; Durmaz ES; Ates E; Sel I; Turgut Gunes S; Kaya OK; Zeynalova A; Kilickesmez O
Eur Radiol; 2020 Feb; 30(2):877-886. PubMed ID: 31691122
[TBL] [Abstract][Full Text] [Related]
29. Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms.
Maurya NS; Kushwaha S; Vetukuri RR; Mani A
Genes (Basel); 2023 Sep; 14(10):. PubMed ID: 37895185
[TBL] [Abstract][Full Text] [Related]
30. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on
He J; Wang Q; Zhang Y; Wu H; Zhou Y; Zhao S
Ann Nucl Med; 2021 May; 35(5):617-627. PubMed ID: 33738763
[TBL] [Abstract][Full Text] [Related]
31. Artificial intelligence based system for predicting permanent stoma after sphincter saving operations.
Kuo CY; Kuo LJ; Lin YK
Sci Rep; 2023 Sep; 13(1):16039. PubMed ID: 37749194
[TBL] [Abstract][Full Text] [Related]
32. A comparative analysis of eight machine learning models for the prediction of lateral lymph node metastasis in patients with papillary thyroid carcinoma.
Feng JW; Ye J; Qi GF; Hong LZ; Wang F; Liu SY; Jiang Y
Front Endocrinol (Lausanne); 2022; 13():1004913. PubMed ID: 36387877
[TBL] [Abstract][Full Text] [Related]
33. 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]
34. LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma.
Feng JW; Ye J; Qi GF; Hong LZ; Wang F; Liu SY; Jiang Y
Front Endocrinol (Lausanne); 2022; 13():1030045. PubMed ID: 36506061
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction.
Güven AT; Özdede M; Şener YZ; Yıldırım AO; Altıntop SE; Yeşilyurt B; Uyaroğlu OA; Tanrıöver MD
Eur J Intern Med; 2023 Aug; 114():74-83. PubMed ID: 37217407
[TBL] [Abstract][Full Text] [Related]
37. Comparison of machine learning classification techniques to predict implantation success in an IVF treatment cycle.
Yiğit P; Bener A; Karabulut S
Reprod Biomed Online; 2022 Nov; 45(5):923-934. PubMed ID: 36088224
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Machine learning algorithms for predicting mortality after coronary artery bypass grafting.
Khalaji A; Behnoush AH; Jameie M; Sharifi A; Sheikhy A; Fallahzadeh A; Sadeghian S; Pashang M; Bagheri J; Ahmadi Tafti SH; Hosseini K
Front Cardiovasc Med; 2022; 9():977747. PubMed ID: 36093147
[TBL] [Abstract][Full Text] [Related]
40. A reliable method for colorectal cancer prediction based on feature selection and support vector machine.
Zhao D; Liu H; Zheng Y; He Y; Lu D; Lyu C
Med Biol Eng Comput; 2019 Apr; 57(4):901-912. PubMed ID: 30478811
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
