Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation.

ABSTRACT: In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms.

SUBMITTER: Tsuji T 

PROVIDER: S-EPMC7371879 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature