Metadata-Version: 1.1
Name: iseeu
Version: 0.1.1
Summary: ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Home-page: https://github.com/williamcaicedo/ISeeU
Author: William Caicedo-Torres
Author-email: UNKNOWN
License: MIT
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
        ```
        
        
Keywords: Deep Learning,Mortality prediction,Shapley values
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
