114 related articles for article (PubMed ID: 33874944)
1. 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]
2. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.
Nhu VH; Shirzadi A; Shahabi H; Singh SK; Al-Ansari N; Clague JJ; Jaafari A; Chen W; Miraki S; Dou J; Luu C; Górski K; Thai Pham B; Nguyen HD; Ahmad BB
Int J Environ Res Public Health; 2020 Apr; 17(8):. PubMed ID: 32316191
[TBL] [Abstract][Full Text] [Related]
3. Application of machine-learning techniques in classification of HIV medical care status for people living with HIV in South Carolina.
Olatosi B; Sun X; Chen S; Zhang J; Liang C; Weissman S; Li X
AIDS; 2021 May; 35(Suppl 1):S19-S28. PubMed ID: 33867486
[TBL] [Abstract][Full Text] [Related]
4. Toxicity prediction of nanoparticles using machine learning approaches.
Ahmadi M; Ayyoubzadeh SM; Ghorbani-Bidkorpeh F
Toxicology; 2024 Jan; 501():153697. PubMed ID: 38056590
[TBL] [Abstract][Full Text] [Related]
5. Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.
Sadjadpour F; Hosseinichimeh N; Abedi V; Soghier LM
Front Public Health; 2024; 12():1380034. PubMed ID: 38864019
[TBL] [Abstract][Full Text] [Related]
6. Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project.
Sakr S; Elshawi R; Ahmed A; Qureshi WT; Brawner C; Keteyian S; Blaha MJ; Al-Mallah MH
PLoS One; 2018; 13(4):e0195344. PubMed ID: 29668729
[TBL] [Abstract][Full Text] [Related]
7. Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.
Lien F; Wang HY; Lu JJ; Wen YH; Chiueh TS
Med Care; 2021 Mar; 59(3):245-250. PubMed ID: 33027237
[TBL] [Abstract][Full Text] [Related]
8. Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.
Akbarzadeh M; Alipour N; Moheimani H; Zahedi AS; Hosseini-Esfahani F; Lanjanian H; Azizi F; Daneshpour MS
J Transl Med; 2022 Apr; 20(1):164. PubMed ID: 35397593
[TBL] [Abstract][Full Text] [Related]
9. Machine learning prediction of tree species diversity using forest structure and environmental factors: a case study from the Hyrcanian forest, Iran.
Valizadeh E; Asadi H; Jaafari A; Tafazoli M
Environ Monit Assess; 2023 Oct; 195(11):1334. PubMed ID: 37851130
[TBL] [Abstract][Full Text] [Related]
10. An artificial intelligence approach to predict infants' health status at birth.
Halomoan Harahap T; Mansouri S; Salim Abdullah O; Uinarni H; Askar S; Jabbar TL; Hussien Alawadi A; Yaseen Hassan A
Int J Med Inform; 2024 Mar; 183():105338. PubMed ID: 38211423
[TBL] [Abstract][Full Text] [Related]
11. A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.
Lin Y; Chen JS; Zhong N; Zhang A; Pan H
BMC Med Res Methodol; 2023 Oct; 23(1):249. PubMed ID: 37880592
[TBL] [Abstract][Full Text] [Related]
12. Prediction of neonatal death in pregnant women in an intensive care unit: Application of machine learning models.
Espinola-Sánchez M; Sanca-Valeriano S; Campaña-Acuña A; Caballero-Alvarado J
Heliyon; 2023 Oct; 9(10):e20693. PubMed ID: 37860503
[TBL] [Abstract][Full Text] [Related]
13. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran).
Tien Bui D; Shirzadi A; Shahabi H; Chapi K; Omidavr E; Pham BT; Talebpour Asl D; Khaledian H; Pradhan B; Panahi M; Bin Ahmad B; Rahmani H; Gróf G; Lee S
Sensors (Basel); 2019 May; 19(11):. PubMed ID: 31146336
[TBL] [Abstract][Full Text] [Related]
14. Prediction of atmospheric PM
Mohammadi F; Teiri H; Hajizadeh Y; Abdolahnejad A; Ebrahimi A
Sci Rep; 2024 Jan; 14(1):2109. PubMed ID: 38267539
[TBL] [Abstract][Full Text] [Related]
15. [A preliminary prediction model of depression based on whole blood cell count by machine learning method].
Yan J; Li XY; Geng YL; Liang YF; Chen C; Han ZW; Zhou R
Zhonghua Yu Fang Yi Xue Za Zhi; 2023 Nov; 57(11):1862-1868. PubMed ID: 38008578
[TBL] [Abstract][Full Text] [Related]
16. Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India.
Idicula-Thomas S; Gawde U; Jha P
BMC Public Health; 2021 Oct; 21(1):1787. PubMed ID: 34607591
[TBL] [Abstract][Full Text] [Related]
17. Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning.
Li S; Zhang J; Hou X; Wang Y; Li T; Xu Z; Chen F; Zhou Y; Wang W; Liu M
J Korean Neurosurg Soc; 2024 Jan; 67(1):94-102. PubMed ID: 37661087
[TBL] [Abstract][Full Text] [Related]
18. Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.
Hassan AA; Muse AH; Chesneau C
Sci Rep; 2024 Mar; 14(1):5956. PubMed ID: 38472298
[TBL] [Abstract][Full Text] [Related]
19. Infant death prediction using machine learning: A population-based retrospective study.
Zhang Z; Xiao Q; Luo J
Comput Biol Med; 2023 Oct; 165():107423. PubMed ID: 37672926
[TBL] [Abstract][Full Text] [Related]
20. Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models.
Shakhssalim N; Talebi A; Pahlevan-Fallahy MT; Sotoodeh K; Alavimajd H; Borumandnia N; Taheri M
Cancer Rep (Hoboken); 2023 Sep; 6(9):e1860. PubMed ID: 37403801
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]