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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)

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Knowledge Discovery for Pervasive and Real-time Intelligent Decision Support in Intensive Care Medicine., , , , , , , and . KMIS, page 241-249. SciTePress, (2011)Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine., , , , , , and . Int. J. Heal. Inf. Syst. Informatics, 9 (3): 36-54 (2014)Improving Quality of Medical Service with Mobile Health Software., , , , , , , , and . EUSPN/ICTH, volume 63 of Procedia Computer Science, page 292-299. Elsevier, (2015)Patients' Admissions in Intensive Care Units: A Clustering Overview., , , , , and . Inf., 8 (1): 23 (2017)Intelligent Decision Support to Predict Patient Barotrauma Risk in Intensive Care Units., , , , , , and . CENTERIS/ProjMAN/HCist, volume 64 of Procedia Computer Science, page 626-634. Elsevier, (2015)Data Mining Models to Predict Patient's Readmission in Intensive Care Units., , , and . ICAART (1), page 604-610. SciTePress, (2014)Using Domain Knowledge to Improve Intelligent Decision Support in Intensive Medicine - A Study of Bacteriological Infections ., , , , , , and . ICAART (2), page 582-587. SciTePress, (2015)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)Assessment of Technology Acceptance in Intensive Care Units., , , , , , and . Int. J. Syst. Serv. Oriented Eng., 4 (3): 26-45 (2014)Patients' Admissions in Intensive Care Units: A Clustering Overview., , , , , and . CBI (2), page 38-44. IEEE, (2016)