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

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

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

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

Authors of this article:

Joon Lee 1 Author Orcid Image

Journals

  1. Magoev K, Krzhizhanovskaya V, Kovalchuk S. Application of clustering methods for detecting critical acute coronary syndrome patients. Procedia Computer Science 2018;136:370 View
  2. Oikonomou E, Williams M, Kotanidis C, Desai M, Marwan M, Antonopoulos A, Thomas K, Thomas S, Akoumianakis I, Fan L, Kesavan S, Herdman L, Alashi A, Centeno E, Lyasheva M, Griffin B, Flamm S, Shirodaria C, Sabharwal N, Kelion A, Dweck M, Van Beek E, Deanfield J, Hopewell J, Neubauer S, Channon K, Achenbach S, Newby D, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019;40(43):3529 View
  3. Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020;2020 View
  4. 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 View
  5. Dai Z, Liu S, Wu J, Li M, Liu J, Li K, Beiki O. Analysis of adult disease characteristics and mortality on MIMIC-III. PLOS ONE 2020;15(4):e0232176 View
  6. Rahman Q, Janmohamed T, Pirbaglou M, Clarke H, Ritvo P, Heffernan J, 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 View
  7. 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 View
  8. Roy A, Bruce C, Schulte P, Olson L, Pola M. Failure prediction using personalized models and an application to heart failure prediction. Big Data Analytics 2020;5(1) View
  9. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  10. Chicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics Journal 2021;27(1):146045822098420 View
  11. Alshwaheen T, Hau Y, Ass'Ad N, Abualsamen M. A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network. IEEE Access 2021;9:3894 View
  12. Wu W, Li Y, Feng A, Li L, Huang T, Xu A, Lyu J. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Military Medical Research 2021;8(1) View

Books/Policy Documents

  1. Berikol G, Berikol G. Artificial Intelligence in Precision Health. View