ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Predicting intervention onset in the ICU with switching state space models.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04713111 (Stress and Recovery in Frontline Healthcare COVID-19 Workers), which is not on this map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Stress and Recovery in Frontline Healthcare COVID-19 Workers: A Feasibility Study Using Wearable and Smartphone Devices
Who cites it
20 citing papers in PubMed, 50 citations in OpenAlex.
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction.Proceedings of machine learning research · 2025Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- Mitigating the impact of biased artificial intelligence in emergency decision-making.Communications medicine · 2022Article
- Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model.Sensors (Basel, Switzerland) · 2022Article
- A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19.Scientific reports · 2022Article
- Early prediction of hemodynamic interventions in the intensive care unit using machine learning.Critical care (London, England) · 2021Article
- Probabilistic Machine Learning for Healthcare.Annual review of biomedical data science · 2021Review
- Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables.Critical care explorations · 2021Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Learning Predictive and Interpretable Timeseries Summaries from ICU Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.Scientific reports · 2020Article
- Predicting Inpatient Medication Orders From Electronic Health Record Data.Clinical pharmacology and therapeutics · 2020Article
- Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Unlocking stress and forecasting its consequences with digital technology.NPJ digital medicine · 2019Article
- The MIMIC Code Repository: enabling reproducibility in critical care research.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Exploring the feasibility of vital signs-based mortality risk prediction in a care facility setting.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
The impact of many intensive care unit interventions has not been fully quantified, especially in heterogeneous patient populations. We train unsupervised switching state autoregressive models on vital signs from the public MIMIC-III database to capture patient movement between physiological states. We compare our learned states to static demographics and raw vital signs in the prediction of five ICU treatments: ventilation, vasopressor administra tion, and three transfusions. We show that our learned states, when combined with demographics and raw vital signs, improve prediction for most interventions even 4 or 8 hours ahead of onset. Our results are competitive with existing work while using a substantially larger and more diverse cohort of 36,050 patients. While custom classifiers can only target a specific clinical event, our model learns physiological states which can help with many interventions. Our robust patient state representations provide a path towards evidence-driven administration of clinical interventions.
Identifiers
28815112PMC5543372W2785720656What 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.