ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Project description
# ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
A ConvNet trained on MIMIC-III data for mortality prediction inside the Intensive Care Unit. It uses a set of 22 predictors sampled during the first 48h of ICU stay to predict the probability of mortality. This set of predictors roughly corresponds to those used by the SAPS-II severity score:
AGE
AIDS
BICARBONATE
BILIRRUBIN
BUN
DIASTOLIC BP
ELECTIVE
FiO2
GCSEyes
GCSMotor
GCSVerbal
HEART RATE
LYMPHOMA
METASTATIC CANCER
PO2
POTASSIUM
SODIUM
SURGICAL
SYSTOLIC BP
TEMPERATURE
URINE OUTPUT
WBC
ISeeU achieves 0.8735 AUROC when evaluated on MIMIC-III. More information is available in our ArXiv [preprint](https://arxiv.org/abs/1901.08201). It also can be installed from PyPi:
`unix pip install iseeu `
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