• Publication: Journal of Biomedical Informatics

Note: This version of ISeeU has been tested with numpy 1.12.1, pandas 0.23.4, keras 2.2.4, deeplift 0.6.6.2, matplotlib 2.0.2 and tensorflow 1.9.0.

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 paper. It also can be installed from PyPi:

pip install iseeu