ArticlePloS one2019
Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- Geriatric Trauma Outcome Score as a Mortality Predictor in Isolated Moderate to Severe Traumatic Brain Injury: A Single-Center Retrospective Study.Healthcare (Basel, Switzerland) · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- Assessing the utility of a sliding-windows deep neural network approach for risk prediction of trauma patients.Scientific reports · 2023Article
- Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.World journal of emergency surgery : WJES · 2023Review
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
- Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.Scientific reports · 2023Article
- Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma.Frontiers in immunology · 2023Article
- Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.Scientific reports · 2022Article
- Prediction of Prognosis in Patients with Trauma by Using Machine Learning.Medicina (Kaunas, Lithuania) · 2022Article
- Decision support by machine learning systems for acute management of severely injured patients: A systematic review.Frontiers in surgery · 2022Review
- Personalized modulation of coagulation factors using a thrombin dynamics model to treat trauma-induced coagulopathy.NPJ systems biology and applications · 2021Article
- Predicting correlated outcomes from molecular data.Bioinformatics (Oxford, England) · 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
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- Severity Trajectories of Pediatric Inpatients Using the Criticality Index.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveMachine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury.
methodsSuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point.
resultsSuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94-0.97), multi-organ failure (overall AUC 0.84-0.90), and transfusion (overall AUC 0.87-0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84-0.89) and venous thromboembolism (overall AUC 0.73-0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48-0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time.
conclusionsMachine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.
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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.