ISeeU

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

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

* Publication: [Journal of Biomedical Informatics](https://www.sciencedirect.com/science/article/abs/pii/S1532046419301881)

**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](https://www.sciencedirect.com/science/article/abs/pii/S1532046419301881). It also can be installed from PyPi:
```unix
pip install iseeu
```

ISeeU

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