iseeu 0.1

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

# 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 `