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

Machine Learning (1501) Clinical Informatics (1050) Clinical Information and Decision Making (1434) Big Data (245) Decision Support for Health Professionals (1210) Electronic Health Records (1068)

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 Lee1 Author Orcid Image
Article Authors Cited by (23) Tweetations (7) Metrics

Journals

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  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
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  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) 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
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  13. Yen J, Lim J. A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review. Sensors 2023;23(1):536 View
  14. Cornelius E, Akman O, Hrozencik D. COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty. Mathematics 2021;9(17):2043 View
  15. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology 2022;39(8) View
  16. Lopez Pineda A, Pourshafeie A, Ioannidis A, Leibold C, Chan A, Bustamante C, Frankovich J, Wojcik G. Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients. Journal of Biomedical Informatics 2021;113:103664 View
  17. Zhang F, Luo C, Lan C, Zhan J. Benchmarking feature selection methods with different prediction models on large-scale healthcare event data. BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2021;1(1):100004 View
  18. Cengil A, Eksioglu B, Eksioglu S, Eswaran H, Hayes C, Bogulski C. Using data analytics for telehealth utilization: A case study in Arkansas. Journal of Telemedicine and Telecare 2023:1357633X2311600 View
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Books/Policy Documents

  1. Berikol G, Berikol G. Artificial Intelligence in Precision Health. View
  2. Miranda E, Kumbangsila M, Aryuni M, Richard , Zakiyyah A, Sano A. Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. View