ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Pooled it
- Article
- Artificial Intelligence and Machine Learning in Reconstructive Microsurgery.Seminars in plastic surgery · 2025Review
- Review
- CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.BMC medical informatics and decision making · 2025Article
- Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory.Scientific reports · 2024Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Personalized event prediction for Electronic Health Records.Artificial intelligence in medicine · 2023Article
- Vision transformer architecture and applications in digital health: a tutorial and survey.Visual computing for industry, biomedicine, and art · 2023Review
- Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.Computer methods and programs in biomedicine update · 2023Article
- Article
- Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.PloS one · 2022Article
- Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021Article
- Modeling multivariate clinical event time-series with recurrent temporal mechanisms.Artificial intelligence in medicine · 2021Article
- Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.Scientific reports · 2020Article
- Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
In intensive care units (ICU), mortality prediction is a critical factor not only for effective medical intervention but also for allocation of clinical resources. Structured electronic health records (EHR) contain valuable information for assessing mortality risk in ICU patients, but current mortality prediction models usually require laborious human-engineered features. Furthermore, substantial missing data in EHR is a common problem for both the construction and implementation of a prediction model.Inspired by language-related models, we design a new framework for dynamic monitoring of patients' mortality risk. Our framework uses the bag-of-words representation for all relevant medical events based on most recent history as inputs. By design, it is robust to missing data in EHR and can be easily implemented as an instant scoring system to monitor the medical development of all ICU patients. Specifically, our model uses latent semantic analysis (LSA) to encode the patients' states into low-dimensional embeddings, which are further fed to long short-term memory networks for mortality risk prediction. Our results show that the deep learning based framework performs better than the existing severity scoring system, SAPS-II. We observe that bidirectional long short-term memory demonstrates superior performance, probably due to the successful capture of both forward and backward temporal dependencies.
Indexed as
Identifiers
31797590PMC6934094W2991357568What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.