Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge

Computers in Cardiology

Volumn 36, Issue , 2009, Pages 541-544

  Moody, George B ^a   Lehman, L H ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL DATA; HIGH-RISK PATIENTS; PATIENT RECORD; WAVE FORMS;

CARDIOLOGY; FORECASTING; TIME SERIES;

INTENSIVE CARE UNITS;

EID: 77952730197     PISSN: 02766574     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000 (June 13);101(23):e215-e220. Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215.
2. Saeed M, Lieu C, Raber G, Mark RG. MIMIC II: A massive temporal ICU patient database to support research in intelligent patient monitoring. Computers in Cardiology 2002; 29:641-644.
3. MIMIC II Database. http://physionet.org/physiobank/-database/mimic2db/.
4. Chen X, Xu D, Zhang G, Mukkamala R. Forecasting acute hypotensive episodes in intensive care patients based on a peripheral arterial blood pressure waveform. Computers in Cardiology 2009;36.
5. Henriques JH, TR Rocha TR. Prediction of acute hypotensive episodes using neural network multi-models. Computers in Cardiology 2009;36.
6. Mneimneh MA, Povinelli RJ. A rule-based approach toward the prediction of acute hypotensive episodes. Computers in Cardiology 2009;36.