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

Toshio Tsuji, Tomonori Nobukawa, Akihisa Mito, Harutoyo Hirano, Zu Soh, Ryota Inokuchi, Etsunori Fujita, Yumi Ogura, Shigehiko Kaneko, Ryuji Nakamura, Noboru Saeki, Masashi Kawamoto, Masao Yoshizumi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%.

Original languageEnglish
Article number11970
JournalScientific reports
Issue number1
Publication statusPublished - 01-12-2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


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