A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.
Kim-Anh-Nhi NguyenPranai TandonSahar GhanavatiSatya Narayana CheetiralaPrem TimsinaRobert M FreemanDavid L ReichMatthew A LevinMadhu MazumdarZahi Adel FayadArash KiaPublished in: JMIR formative research (2023)
We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- risk assessment
- artificial intelligence
- healthcare
- convolutional neural network
- cardiac arrest
- machine learning
- high resolution
- big data
- palliative care
- quality improvement
- early onset
- human health
- heavy metals
- pain management
- photodynamic therapy
- chronic pain
- climate change
- fluorescence imaging