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Citing this Article

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Published on 17.01.17 in Vol 5, No 1 (2017): Jan-Mar

This paper is in the following e-collection/theme issue:

Works citing "Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill"

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.6690):

(note that this is only a small subset of citations)

  1. Lin K, Hu Y, Kong G. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. International Journal of Medical Informatics 2019;125:55
  2. Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019;
  3. Li Q, Xu Y. VS-GRU: A Variable Sensitive Gated Recurrent Neural Network for Multivariate Time Series with Massive Missing Values. Applied Sciences 2019;9(15):3041
  4. Magoev K, Krzhizhanovskaya VV, Kovalchuk SV. Application of clustering methods for detecting critical acute coronary syndrome patients. Procedia Computer Science 2018;136:370
  5. Rahman QA, Janmohamed T, Pirbaglou M, Clarke H, Ritvo P, Heffernan JM, Katz J. Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods. Journal of Medical Internet Research 2018;20(11):e12001