Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.
Akram MohammedPradeep S B PodilaRobert L DavisKenneth I AtagaJane S HankinsRishikesan KamaleswaranPublished in: Journal of medical Internet research (2020)
This retrospective study demonstrated the viability of using machine learning to predict acute organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.
Keyphrases
- early onset
- sickle cell disease
- intensive care unit
- liver failure
- machine learning
- healthcare
- late onset
- end stage renal disease
- ejection fraction
- respiratory failure
- quality improvement
- chronic kidney disease
- drug induced
- small molecule
- aortic dissection
- primary care
- palliative care
- hepatitis b virus
- high throughput
- risk assessment
- patient reported outcomes
- human health
- climate change
- patient reported
- deep learning
- health insurance