ArticleScientific reports2020
Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.BMC medical informatics and decision making · 2026Article
- Development and explanation of electrocardiogram-based deep learning for predicting short-term mortality in heart failure patients.Journal of global health · 2026Article
- Thiamine use and short-term prognosis in critical cerebrovascular disease: a propensity score matched retrospective analysis from the MIMIC-IV database.BMC neurology · 2025Observational
- Continuous vital sign monitoring for predicting hospital length of stay: a feasibility study in chronic obstructive pulmonary disease and chronic heart failure patients.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Review
- Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.BMC medical informatics and decision making · 2025Article
- Early prediction of mortality upon intensive care unit admission.BMC medical informatics and decision making · 2024Article
- Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.Critical care (London, England) · 2024Article
- A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study.JMIR human factors · 2024Article
- Variables associated to intensive care unit (ICU)-mortality among patients admitted to surgical intensive care unit in Ethiopia: a retrospective observational study.BMC anesthesiology · 2023Observational
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients.International journal of general medicine · 2023Article
- A Novel Preoperative Prediction Model Based on Deep Learning to Predict Neoplasm T Staging and Grading in Patients with Upper Tract Urothelial Carcinoma.Journal of clinical medicine · 2022Article
- Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.BMC medical informatics and decision making · 2022Article
- Artificial intelligence in perioperative medicine: a narrative review.Korean journal of anesthesiology · 2022Review
- Mortality Prediction in the ICU: The Daunting Task of Predicting the Unpredictable.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2022Article
- Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.Digital healthArticle
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3 authors.
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Abstract
Mortality risk prediction can greatly improve the utilization of resources in intensive care units (ICUs). Existing schemes in ICUs today require laborious manual input of many complex parameters. In this work, we present a scheme that uses variations in vital signs over a 24-h period to make mortality risk assessments for 3-day, 7-day, and 14-day windows. We develop a hybrid neural network model that combines convolutional (CNN) layers with bidirectional long short-term memory (BiLSTM) to predict mortality from statistics describing the variation of heart rate, blood pressure, respiratory rate, blood oxygen levels, and temperature. Our scheme performs strongly compared to state-of-the-art schemes in the literature for mortality prediction, with our highest-performing model achieving an area under the receiver-operator curve of 0.884. We conclude that the use of a hybrid CNN-BiLSTM network is highly effective in determining mortality risk for the 3, 7, and 14 day windows from vital signs. As vital signs are routinely recorded, in many cases automatically, our scheme could be implemented such that highly accurate mortality risk could be predicted continuously and automatically, reducing the burden on healthcare providers and improving patient outcomes.
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