These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
203 related articles for article (PubMed ID: 30957270)
1. Using machine-learning methods to support health-care professionals in making admission decisions. Luo L; Li J; Liu C; Shen W Int J Health Plann Manage; 2019 Apr; 34(2):e1236-e1246. PubMed ID: 30957270 [TBL] [Abstract][Full Text] [Related]
2. Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease. Jiang H; Mao H; Lu H; Lin P; Garry W; Lu H; Yang G; Rainer TH; Chen X Int J Med Inform; 2021 Jan; 145():104326. PubMed ID: 33197878 [TBL] [Abstract][Full Text] [Related]
3. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure. Qiu H; Luo L; Su Z; Zhou L; Wang L; Chen Y BMC Med Inform Decis Mak; 2020 May; 20(1):83. PubMed ID: 32357880 [TBL] [Abstract][Full Text] [Related]
4. How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach. Ichikawa D; Saito T; Ujita W; Oyama H J Biomed Inform; 2016 Dec; 64():20-24. PubMed ID: 27658886 [TBL] [Abstract][Full Text] [Related]
6. Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme. Shimoda A; Ichikawa D; Oyama H Comput Methods Programs Biomed; 2018 Sep; 163():39-46. PubMed ID: 30119856 [TBL] [Abstract][Full Text] [Related]
7. Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree. Li K; Shi Q; Liu S; Xie Y; Liu J Medicine (Baltimore); 2021 May; 100(19):e25813. PubMed ID: 34106618 [TBL] [Abstract][Full Text] [Related]
8. Emergency department triage prediction of clinical outcomes using machine learning models. Raita Y; Goto T; Faridi MK; Brown DFM; Camargo CA; Hasegawa K Crit Care; 2019 Feb; 23(1):64. PubMed ID: 30795786 [TBL] [Abstract][Full Text] [Related]
9. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719 [TBL] [Abstract][Full Text] [Related]
10. [Prediction of intensive care unit readmission for critically ill patients based on ensemble learning]. Lin Y; Wu JY; Lin K; Hu YH; Kong GL Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Jun; 53(3):566-572. PubMed ID: 34145862 [TBL] [Abstract][Full Text] [Related]
11. Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance. Tian X; Chong Y; Huang Y; Guo P; Li M; Zhang W; Du Z; Li X; Hao Y Comput Math Methods Med; 2019; 2019():6915850. PubMed ID: 31281411 [TBL] [Abstract][Full Text] [Related]
12. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. Dinh A; Miertschin S; Young A; Mohanty SD BMC Med Inform Decis Mak; 2019 Nov; 19(1):211. PubMed ID: 31694707 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. [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]
15. Predicting hospitalization following psychiatric crisis care using machine learning. Blankers M; van der Post LFM; Dekker JJM BMC Med Inform Decis Mak; 2020 Dec; 20(1):332. PubMed ID: 33302948 [TBL] [Abstract][Full Text] [Related]
16. Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework. Wang C; Liu L; Xu C; Lv W Int J Environ Res Public Health; 2019 Jan; 16(3):. PubMed ID: 30691063 [TBL] [Abstract][Full Text] [Related]
17. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Huang JC; Tsai YC; Wu PY; Lien YH; Chien CY; Kuo CF; Hung JF; Chen SC; Kuo CH Comput Methods Programs Biomed; 2020 Oct; 195():105536. PubMed ID: 32485511 [TBL] [Abstract][Full Text] [Related]
18. Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach. Viana Dos Santos Santana Í; Cm da Silveira A; Sobrinho Á; Chaves E Silva L; Dias da Silva L; Santos DFS; Gurjão EC; Perkusich A J Med Internet Res; 2021 Apr; 23(4):e27293. PubMed ID: 33750734 [TBL] [Abstract][Full Text] [Related]
19. Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning. Mbarek L; Chen S; Jin A; Pan Y; Meng X; Yang X; Xu Z; Jiang Y; Wang Y Eur J Med Res; 2024 Oct; 29(1):494. PubMed ID: 39385211 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]