title: Prediction of acute hypotensive episodes by means of neural network multi-models.
abstract: This work proposes the application of neural network multi-models to the prediction of adverse acute hypotensive episodes (AHE) occurring in intensive care units (ICU). A generic methodology consisting of two phases is considered. In the first phase, a correlation analysis between the current blood pressure time signal and a collection of historical blood pressure templates is carried out. From this procedure the most similar signals are determined and the respective prediction neural models, previously trained, selected. Then, in a second phase, the multi-model structure is employed to predict the future evolution of current blood pressure signal, enabling to detect the occurrence of an AHE. The effectiveness of the methodology was validated in the context of the 10th PhysioNet/Computers in Cardiology Challenge-Predicting Acute Hypotensive Episodes, applied to a specific set of blood pressure signals, available in MIMIC-II database. A correct prediction of 10 out of 10 AHE for event 1 and of 37 out of 40 AHE for event 2 was achieved, corresponding to the best results of all entries in the two events of the challenge. The generalization capabilities of the strategy was confirmed by applying it to an extended dataset of blood pressure signals, also collected from the MIMIC-II database. A total of 2344 examples, selected from 311 blood pressure signals were tested, enabling to obtain a global sensitivity of 82.8% and a global specificity of 78.4%.
authors: Instituto Superior de Engenharia de Coimbra, Departamento de Engenharia Informática e de Sistemas, Coimbra, Portugal.
authors: Teresa
authors: T
authors: Rocha
authors: Simão
authors: S
authors: Paredes
authors: Paulo
authors: P
authors: de Carvalho
authors: Jorge
authors: J
authors: Henriques
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007022:Hypotension / Q000175:diagnosis* / Q000503:physiopathology; D007362:Intensive Care Units; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted