ArticleSensors (Basel, Switzerland)2021
Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 43 citations in OpenAlex.
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Fast and interpretable mortality risk scores for critical care patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Review
- Structural Magnetic Resonance Imaging Brain Age Investigation in Athletes with Persistent Postconcussion Syndrome.Neurotrauma reports · 2025Article
- Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.Diagnostics (Basel, Switzerland) · 2024Article
- Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling.Parasites & vectors · 2023Article
- Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.International journal of environmental research and public health · 2023Article
- Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.International journal of environmental research and public health · 2023Article
- Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.Sensors (Basel, Switzerland) · 2023Article
- Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning.Biomedicines · 2023Article
- Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.Journal of clinical medicine · 2022Article
- Prediction of Mortality after Burn Surgery in Critically Ill Burn Patients Using Machine Learning Models.Journal of personalized medicine · 2022Article
- COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients.Diagnostics (Basel, Switzerland) · 2022Article
- Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.Frontiers in cellular and infection microbiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18-45, 45-65, 65-85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient's health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned.
Indexed as
Identifiers
What Socratic 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 Socratic graph.