ArticleIEEE journal of biomedical and health informatics2019
Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.
Article in IEEE journal of biomedical and health informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Electronic "Sniffer" Systems to Identify the Acute Respiratory Distress Syndrome.Annals of the American Thoracic Society · 2019Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- Multimodal Deep Learning for ARDS Detection.medRxiv : the preprint server for health sciences · 2025Article
- Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.Critical care medicine · 2025Article
- Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan.Annals of intensive care · 2023Article
- Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.NPJ digital medicine · 2023Article
- Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.Frontiers in neurology · 2023Article
- Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.Intelligence-based medicine · 2023Article
- Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP).Diagnostics (Basel, Switzerland) · 2022Article
- EMD-Based Method for Supervised Classification of Parkinson's Disease Patients Using Balance Control Data.Bioengineering (Basel, Switzerland) · 2022Article
- Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables.Critical care explorations · 2021Article
- Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.Critical care explorations · 2021Article
- Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).Annals of operations research · 2021Article
- Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome.IEEE journal of biomedical and health informatics · 2021Article
- Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?Cureus · 2021Review
- Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.Critical care explorations · 2021Article
- Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?American journal of respiratory and critical care medicine · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may have less confidence when assigning a medical diagnosis to some patients because of ambiguity in the patient's case or imperfect reliability of the diagnostic criteria. As a result, some cases used in algorithm training may be mislabeled, adversely affecting the algorithm's performance. However, experts may also be able to quantify their diagnostic uncertainty in these cases. We present a robust method implemented with support vector machines (SVM) to account for such clinical diagnostic uncertainty when training an algorithm to detect patients who develop the acute respiratory distress syndrome (ARDS). ARDS is a syndrome of the critically ill that is diagnosed using clinical criteria known to be imperfect. We represent uncertainty in the diagnosis of ARDS as a graded weight of confidence associated with each training label. We also performed a novel time-series sampling method to address the problem of intercorrelation among the longitudinal clinical data from each patient used in model training to limit overfitting. Preliminary results show that we can achieve meaningful improvement in the performance of algorithm to detect patients with ARDS on a hold-out sample, when we compare our method that accounts for the uncertainty of training labels with a conventional SVM algorithm.
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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.