556 related articles for article (PubMed ID: 34906845)
1. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Shi H; Yang D; Tang K; Hu C; Li L; Zhang L; Gong T; Cui Y
Clin Nutr; 2022 Jan; 41(1):202-210. PubMed ID: 34906845
[TBL] [Abstract][Full Text] [Related]
2. Risk factors of malnutrition in Chinese children with congenital heart defect.
Zhang M; Wang L; Huang R; Sun C; Bao N; Xu Z
BMC Pediatr; 2020 May; 20(1):213. PubMed ID: 32404077
[TBL] [Abstract][Full Text] [Related]
3. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.
Dai C; Fan Y; Li Y; Bao X; Li Y; Su M; Yao Y; Deng K; Xing B; Feng F; Feng M; Wang R
Front Endocrinol (Lausanne); 2020; 11():643. PubMed ID: 33042013
[No Abstract] [Full Text] [Related]
4. Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms.
Anku EK; Duah HO
PLoS One; 2024; 19(2):e0296625. PubMed ID: 38349921
[TBL] [Abstract][Full Text] [Related]
5. Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.
Tong C; Du X; Chen Y; Zhang K; Shan M; Shen Z; Zhang H; Zheng J
Int J Surg; 2024 Apr; 110(4):2207-2216. PubMed ID: 38265429
[TBL] [Abstract][Full Text] [Related]
6. Nutritional status of pediatric patients with congenital heart disease: pre- and post cardiac surgery.
Ratanachu-Ek S; Pongdara A
J Med Assoc Thai; 2011 Aug; 94 Suppl 3():S133-7. PubMed ID: 22043766
[TBL] [Abstract][Full Text] [Related]
7. Explainable machine-learning predictions for complications after pediatric congenital heart surgery.
Zeng X; Hu Y; Shu L; Li J; Duan H; Shu Q; Li H
Sci Rep; 2021 Aug; 11(1):17244. PubMed ID: 34446783
[TBL] [Abstract][Full Text] [Related]
8. The effect of preoperative nutritional status on postoperative outcomes in children undergoing surgery for congenital heart defects in San Francisco (UCSF) and Guatemala City (UNICAR).
Radman M; Mack R; Barnoya J; Castañeda A; Rosales M; Azakie A; Mehta N; Keller R; Datar S; Oishi P; Fineman J
J Thorac Cardiovasc Surg; 2014 Jan; 147(1):442-50. PubMed ID: 23583172
[TBL] [Abstract][Full Text] [Related]
9. Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients.
Wang K; Yan LZ; Li WZ; Jiang C; Wang NN; Zheng Q; Dong NG; Shi JW
Front Cardiovasc Med; 2022; 9():863642. PubMed ID: 35800164
[TBL] [Abstract][Full Text] [Related]
10. Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study.
Wang X; Yang F; Zhu M; Cui H; Wei J; Li J; Chen W
J Med Internet Res; 2023 Mar; 25():e42435. PubMed ID: 36917167
[TBL] [Abstract][Full Text] [Related]
11. Prediction of the development of acute kidney injury following cardiac surgery by machine learning.
Tseng PY; Chen YT; Wang CH; Chiu KM; Peng YS; Hsu SP; Chen KL; Yang CY; Lee OK
Crit Care; 2020 Jul; 24(1):478. PubMed ID: 32736589
[TBL] [Abstract][Full Text] [Related]
12. Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer.
Hsu WH; Ko AT; Weng CS; Chang CL; Jan YT; Lin JB; Chien HJ; Lin WC; Sun FJ; Wu KP; Lee J
J Cachexia Sarcopenia Muscle; 2023 Oct; 14(5):2044-2053. PubMed ID: 37435785
[TBL] [Abstract][Full Text] [Related]
13. Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study.
Su PY; Wei YC; Luo H; Liu CH; Huang WY; Chen KF; Lin CP; Wei HY; Lee TH
JMIR Med Inform; 2022 Mar; 10(3):e32508. PubMed ID: 35072631
[TBL] [Abstract][Full Text] [Related]
14. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
Huang J; Jin W; Duan X; Liu X; Shu T; Fu L; Deng J; Chen H; Liu G; Jiang Y; Liu Z
Front Public Health; 2022; 10():1086339. PubMed ID: 36711330
[TBL] [Abstract][Full Text] [Related]
15. Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.
Rahman SMJ; Ahmed NAMF; Abedin MM; Ahammed B; Ali M; Rahman MJ; Maniruzzaman M
PLoS One; 2021; 16(6):e0253172. PubMed ID: 34138925
[TBL] [Abstract][Full Text] [Related]
16. [Effect of calorie-enriched formula on postoperative catch-up growth in infants with cyanotic congenital heart disease: a propective randomized controlled study].
Zhou HM; Qiu Q; Yan MH; Peng LJ; Fang JJ; Zhou BJ; Yang SS; Zhang GC
Zhongguo Dang Dai Er Ke Za Zhi; 2021 Jan; 23(1):78-83. PubMed ID: 33476542
[TBL] [Abstract][Full Text] [Related]
17. Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit.
Deshmukh F; Merchant SS
Am J Gastroenterol; 2020 Oct; 115(10):1657-1668. PubMed ID: 32341266
[TBL] [Abstract][Full Text] [Related]
18. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
Du Z; Yang Y; Zheng J; Li Q; Lin D; Li Y; Fan J; Cheng W; Chen XH; Cai Y
JMIR Med Inform; 2020 Jul; 8(7):e17257. PubMed ID: 32628616
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears.
Li C; Alike Y; Hou J; Long Y; Zheng Z; Meng K; Yang R
Knee Surg Sports Traumatol Arthrosc; 2023 Jul; 31(7):2615-2623. PubMed ID: 36629889
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
