Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation
| Autor: | Ryota Inokuchi, Zu Soh, Akihisa Mito, Toshio Tsuji, Masashi Kawamoto, Shigehiko Kaneko, Tomonori Nobukawa, Ryuji Nakamura, Noboru Saeki, Masao Yoshizumi, Yumi Ogura, Etsunori Fujita, Harutoyo Hirano |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Time Factors lcsh:Medicine Blood Pressure 030204 cardiovascular system & hematology 01 natural sciences Article 03 medical and health sciences Probabilistic neural network 0302 clinical medicine Heart Rate Internal medicine Medicine Humans lcsh:Science Hidden Markov model Event (probability theory) Probability Multidisciplinary Artificial neural network business.industry lcsh:R 010401 analytical chemistry Mixture model medicine.disease Prognosis 0104 chemical sciences Term (time) Blood pressure Databases as Topic Ventricular fibrillation Acute Disease Ventricular Fibrillation Cardiology lcsh:Q Neural Networks Computer Hypotension business Biomedical engineering |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020) |
| ISSN: | 2045-2322 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|