Tabular Classification
Scikit-learn
English
hierarchical
healthcare
ehr
copd
clinical-risk
tabular
scikit-learn
clustering
unsupervised
Instructions to use stormid/copd-model-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use stormid/copd-model-e with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("stormid/copd-model-e", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| # Mappings | |
| This folder contains a range of mappings used within the processing stages of model E: | |
| - `inhaler_mapping.json`: Inhaler mappings for any Chapter 3 BNF code inhaler prescriptions present in the SafeHaven prescribing dataset. Information on NHS inhaler types, found [here](https://www.coch.nhs.uk/media/172781/3-respiratory-system.pdf), was used to create the mapping. | |
| - `test_mapping.json`: A mapping created for any of the top 20 most frequently occurring lab tests, plus any lab tests found relevant for indicating COPD severity in Model A. This mapping creates a common name for a specific test and lists any related names the test may appear under within the SCI Store dataset. | |
| - `Comorbidity feature review for models & clin summary update v2 May 2021.xlsx`: A mapping between diagnosis names found in SMR01 and their associated comorbidities (taken from Model A). | |
| - `diag_copd_resp_desc.json`: DIAGDesc for COPD and respiratory admissions |