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Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units .

, , , , , , and . KMIS, page 245-254. SciTePress, (2014)

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Using Domain Knowledge to Improve Intelligent Decision Support in Intensive Medicine - A Study of Bacteriological Infections ., , , , , , and . ICAART (2), page 582-587. SciTePress, (2015)Data Mining Models to Predict Patient's Readmission in Intensive Care Units., , , and . ICAART (1), page 604-610. SciTePress, (2014)Assessment of Technology Acceptance in Intensive Care Units., , , , , , and . Int. J. Syst. Serv. Oriented Eng., 4 (3): 26-45 (2014)Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients., , , , , , and . Int. J. Heal. Inf. Syst. Informatics, 8 (4): 1-16 (2013)Resurgery Clusters in Intensive Medicine., , , , , , and . EUSPN/ICTH, volume 98 of Procedia Computer Science, page 528-533. Elsevier, (2016)Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients., , , , , and . AmIHEALTH, volume 9456 of Lecture Notes in Computer Science, page 77-90. Springer, (2015)Critical Events in Mechanically Ventilated Patients., , , , , and . WorldCIST (2), volume 445 of Advances in Intelligent Systems and Computing, page 589-598. Springer, (2016)Patients' Admissions in Intensive Care Units: A Clustering Overview., , , , , and . CBI (2), page 38-44. IEEE, (2016)Pervasive Information Systems to Intensive Care Medicine - Technology Acceptance Model., , , , , , , and . ICEIS (1), page 177-184. SciTePress, (2013)Categorize Readmitted Patients in Intensive Medicine by Means of Clustering Data Mining., , , , , , and . Int. J. E Health Medical Commun., 8 (3): 22-37 (2017)