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.
2061 related articles for article (PubMed ID: 29132626)
1. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Awad A; Bader-El-Den M; McNicholas J; Briggs J Int J Med Inform; 2017 Dec; 108():185-195. PubMed ID: 29132626 [TBL] [Abstract][Full Text] [Related]
2. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Pirracchio R; Petersen ML; Carone M; Rigon MR; Chevret S; van der Laan MJ Lancet Respir Med; 2015 Jan; 3(1):42-52. PubMed ID: 25466337 [TBL] [Abstract][Full Text] [Related]
3. Developing machine learning models for prediction of mortality in the medical intensive care unit. Nistal-Nuño B Comput Methods Programs Biomed; 2022 Apr; 216():106663. PubMed ID: 35123348 [TBL] [Abstract][Full Text] [Related]
4. Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study. Nistal-Nuño B Einstein (Sao Paulo); 2024; 22():eAO0467. PubMed ID: 39417479 [TBL] [Abstract][Full Text] [Related]
5. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. Lin K; Hu Y; Kong G Int J Med Inform; 2019 May; 125():55-61. PubMed ID: 30914181 [TBL] [Abstract][Full Text] [Related]
6. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU. Kong G; Lin K; Hu Y BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381 [TBL] [Abstract][Full Text] [Related]
7. Predictive performance of quick Sepsis-related Organ Failure Assessment for mortality and ICU admission in patients with infection at the ED. Wang JY; Chen YX; Guo SB; Mei X; Yang P Am J Emerg Med; 2016 Sep; 34(9):1788-93. PubMed ID: 27321936 [TBL] [Abstract][Full Text] [Related]
8. A comparative study of four intensive care outcome prediction models in cardiac surgery patients. Doerr F; Badreldin AM; Heldwein MB; Bossert T; Richter M; Lehmann T; Bayer O; Hekmat K J Cardiothorac Surg; 2011 Mar; 6():21. PubMed ID: 21362175 [TBL] [Abstract][Full Text] [Related]
9. [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]
10. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. Davoodi R; Moradi MH J Biomed Inform; 2018 Mar; 79():48-59. PubMed ID: 29471111 [TBL] [Abstract][Full Text] [Related]
11. Prognostic Accuracy of the SOFA Score, SIRS Criteria, and qSOFA Score for In-Hospital Mortality Among Adults With Suspected Infection Admitted to the Intensive Care Unit. Raith EP; Udy AA; Bailey M; McGloughlin S; MacIsaac C; Bellomo R; Pilcher DV; JAMA; 2017 Jan; 317(3):290-300. PubMed ID: 28114553 [TBL] [Abstract][Full Text] [Related]
12. Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients. Zaidi H; Bader-El-Den M; McNicholas J BMC Public Health; 2019 Sep; 19(1):1231. PubMed ID: 31488143 [TBL] [Abstract][Full Text] [Related]
13. Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction. Nistal-Nuño B J Clin Monit Comput; 2022 Jun; 36(3):751-763. PubMed ID: 33860407 [TBL] [Abstract][Full Text] [Related]
14. The Pros and Cons of the Prediction Game: The Never-ending Debate of Mortality in the Intensive Care Unit. Fuchs PA; Czech IJ; Krzych ŁJ Int J Environ Res Public Health; 2019 Sep; 16(18):. PubMed ID: 31540201 [TBL] [Abstract][Full Text] [Related]
15. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. Mao Q; Jay M; Hoffman JL; Calvert J; Barton C; Shimabukuro D; Shieh L; Chettipally U; Fletcher G; Kerem Y; Zhou Y; Das R BMJ Open; 2018 Jan; 8(1):e017833. PubMed ID: 29374661 [TBL] [Abstract][Full Text] [Related]
16. A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. Xia J; Pan S; Zhu M; Cai G; Yan M; Su Q; Yan J; Ning G Comput Math Methods Med; 2019; 2019():8152713. PubMed ID: 31827589 [TBL] [Abstract][Full Text] [Related]
17. Comparison of the APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome. Cho DY; Wang YC Intensive Care Med; 1997 Jan; 23(1):77-84. PubMed ID: 9037644 [TBL] [Abstract][Full Text] [Related]
18. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Thorsen-Meyer HC; Nielsen AB; Nielsen AP; Kaas-Hansen BS; Toft P; Schierbeck J; Strøm T; Chmura PJ; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Belling K; Brunak S; Perner A Lancet Digit Health; 2020 Apr; 2(4):e179-e191. PubMed ID: 33328078 [TBL] [Abstract][Full Text] [Related]
19. Customization of a Severity of Illness Score Using Local Electronic Medical Record Data. Lee J; Maslove DM J Intensive Care Med; 2017 Jan; 32(1):38-47. PubMed ID: 25969432 [TBL] [Abstract][Full Text] [Related]
20. Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: a single-center retrospective study. Chan DXH; Sim YE; Chan YH; Poopalalingam R; Abdullah HR BMJ Open; 2018 Mar; 8(3):e019427. PubMed ID: 29574442 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]