316 related articles for article (PubMed ID: 34261070)
1. Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.
Mangold C; Zoretic S; Thallapureddy K; Moreira A; Chorath K; Moreira A
Neonatology; 2021; 118(4):394-405. PubMed ID: 34261070
[TBL] [Abstract][Full Text] [Related]
2. NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death.
Teji JS; Jain S; Gupta SK; Suri JS
Comput Biol Med; 2022 Aug; 147():105639. PubMed ID: 35635905
[TBL] [Abstract][Full Text] [Related]
3. Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.
Hamilton EF; Dyachenko A; Ciampi A; Maurel K; Warrick PA; Garite TJ
J Matern Fetal Neonatal Med; 2020 Jan; 33(1):73-80. PubMed ID: 29886760
[No Abstract] [Full Text] [Related]
4. Prediction of neonatal deaths in NICUs: development and validation of machine learning models.
Sheikhtaheri A; Zarkesh MR; Moradi R; Kermani F
BMC Med Inform Decis Mak; 2021 Apr; 21(1):131. PubMed ID: 33874944
[TBL] [Abstract][Full Text] [Related]
5. The future of Cochrane Neonatal.
Soll RF; Ovelman C; McGuire W
Early Hum Dev; 2020 Nov; 150():105191. PubMed ID: 33036834
[TBL] [Abstract][Full Text] [Related]
6. Development and Validation of a Mortality Prediction Model in Extremely Low Gestational Age Neonates.
Moreira A; Benvenuto D; Fox-Good C; Alayli Y; Evans M; Jonsson B; Hakansson S; Harper N; Kim J; Norman M; Bruschettini M
Neonatology; 2022; 119(4):418-427. PubMed ID: 35598593
[TBL] [Abstract][Full Text] [Related]
7. Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma.
Han T; Xiong F; Sun B; Zhong L; Han Z; Lei M
Int J Med Inform; 2024 Apr; 184():105383. PubMed ID: 38387198
[TBL] [Abstract][Full Text] [Related]
8. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.
Chung H; Ko H; Kang WS; Kim KW; Lee H; Park C; Song HO; Choi TY; Seo JH; Lee J
J Med Internet Res; 2021 Apr; 23(4):e27060. PubMed ID: 33764883
[TBL] [Abstract][Full Text] [Related]
9. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.
Silva Rocha ED; de Morais Melo FL; de Mello MEF; Figueiroa B; Sampaio V; Endo PT
BMC Med Inform Decis Mak; 2022 Dec; 22(1):334. PubMed ID: 36536413
[TBL] [Abstract][Full Text] [Related]
10. Neonatal mortality prediction with routinely collected data: a machine learning approach.
Batista AFM; Diniz CSG; Bonilha EA; Kawachi I; Chiavegatto Filho ADP
BMC Pediatr; 2021 Jul; 21(1):322. PubMed ID: 34289819
[TBL] [Abstract][Full Text] [Related]
11. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.
Liu JH; Shih CY; Huang HL; Peng JK; Cheng SY; Tsai JS; Lai F
J Med Internet Res; 2023 Aug; 25():e47366. PubMed ID: 37594793
[TBL] [Abstract][Full Text] [Related]
12. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.
Shakibfar S; Nyberg F; Li H; Zhao J; Nordeng HME; Sandve GKF; Pavlovic M; Hajiebrahimi M; Andersen M; Sessa M
Front Public Health; 2023; 11():1183725. PubMed ID: 37408750
[TBL] [Abstract][Full Text] [Related]
13. Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.
Chiasakul T; Lam BD; McNichol M; Robertson W; Rosovsky RP; Lake L; Vlachos IS; Adamski A; Reyes N; Abe K; Zwicker JI; Patell R
Eur J Haematol; 2023 Dec; 111(6):951-962. PubMed ID: 37794526
[TBL] [Abstract][Full Text] [Related]
14. The impact of antenatal care on neonatal mortality in sub-Saharan Africa: A systematic review and meta-analysis.
Tekelab T; Chojenta C; Smith R; Loxton D
PLoS One; 2019; 14(9):e0222566. PubMed ID: 31518365
[TBL] [Abstract][Full Text] [Related]
15. Artificial intelligence and prediction of cardiometabolic disease: Systematic review of model performance and potential benefits in indigenous populations.
Jeong K; Mallard AR; Coombe L; Ward J
Artif Intell Med; 2023 May; 139():102534. PubMed ID: 37100506
[TBL] [Abstract][Full Text] [Related]
16. Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania.
Mboya IB; Mahande MJ; Mohammed M; Obure J; Mwambi HG
BMJ Open; 2020 Oct; 10(10):e040132. PubMed ID: 33077570
[TBL] [Abstract][Full Text] [Related]
17. Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis.
Baashar Y; Alkawsi G; Alhussian H; Capretz LF; Alwadain A; Alkahtani AA; Almomani M
Comput Intell Neurosci; 2022; 2022():5849995. PubMed ID: 35251153
[TBL] [Abstract][Full Text] [Related]
18. Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix.
Bahado-Singh RO; Sonek J; McKenna D; Cool D; Aydas B; Turkoglu O; Bjorndahl T; Mandal R; Wishart D; Friedman P; Graham SF; Yilmaz A
Ultrasound Obstet Gynecol; 2019 Jul; 54(1):110-118. PubMed ID: 30381856
[TBL] [Abstract][Full Text] [Related]
19. Improving preterm newborn identification in low-resource settings with machine learning.
Rittenhouse KJ; Vwalika B; Keil A; Winston J; Stoner M; Price JT; Kapasa M; Mubambe M; Banda V; Muunga W; Stringer JSA
PLoS One; 2019; 14(2):e0198919. PubMed ID: 30811399
[TBL] [Abstract][Full Text] [Related]
20. Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review.
Cha Y; Kim JT; Park CH; Kim JW; Lee SY; Yoo JI
J Orthop Surg Res; 2022 Dec; 17(1):520. PubMed ID: 36456982
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
