Analyze factors related to readmission as well as other outcomes pertaining to patients in order to classify a patient-hospital outcome
3 different outputs:
-
No readmission
-
A readmission in less than 30 days (this situation is not good, because maybe your treatment was not appropriate);
-
A readmission in more than 30 days (this one is not so good as well the last one, however, the reason could be the state of the patient.
How effective was the treatment received in hospital?
Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios, and John N. Clore, “Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records,” BioMed Research International, vol. 2014, Article ID 781670, 11 pages, 2014.
https://www.hindawi.com/journals/bmri/2014/781670/
https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008#
https://www.hindawi.com/journals/bmri/2014/781670/tab1/
note: I use plotly for make and interactive plots, but can be seen if you open the notebooks on github.
1-readmited_preprocess.ipynb - Data loading and cleaning
2-readmited_data_explore.ipynb - Data explore and analysis
3-readmited_Models_imbalance.ipynb (Selected Model) - Modeling with a imbalanced dataset.
Modeling conclusions
4-readmited_Models_balanced.ipynb - Modeling using smote for balanced the dataset
5-readmited_Models_imbalance_NoMeds.ipynb - Modeling with imbalanced dataset and no Medication features
The selected evaluation metric is the
Recall - Sensitivity
A False Negative might delay more tests or treatment, however a False Positive may just lead to more tests or treatments – not as costly as putting a life at stake.
the selected model is the result of process detailed in 3-readmited_Models_imbalance.ipynb
The model is saved in the models folder:
NB_pipeline_imbalance_recall.joblib
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience