ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis.
Ana CysneirosTiago GalvãoNuno DominguesPedro JorgeLuis BentoIgnacio Martín-LoechesPublished in: Biomedicines (2024)
Integrating data available in all intensive care units enables the prediction of C-ARDS mortality by utilizing evolving P/F ratios and CXR. This approach can assist in tailoring treatment plans and initiating early discussions to escalate care and extracorporeal life support. Machine learning algorithms for imaging classification can uncover otherwise inaccessible patterns, potentially evolving into another form of ARDS phenotyping. The combined features of these algorithms and clinical variables demonstrate superior performance compared to either element alone.
Keyphrases
- machine learning
- big data
- acute respiratory distress syndrome
- mechanical ventilation
- deep learning
- extracorporeal membrane oxygenation
- intensive care unit
- artificial intelligence
- cardiovascular events
- healthcare
- electronic health record
- high throughput
- health insurance
- quality improvement
- cardiovascular disease
- coronary artery disease
- chronic pain
- data analysis
- mass spectrometry
- photodynamic therapy
- affordable care act