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Project description
deep-ehr-graph
Description
This project aims at demonstring deep learning methodologies for EHR data. The use case is to predict different outcomes for patients in the ICU. The dataset is from (MIMIC-IV demo)
Installation
With pip
pip3 install -U deepehrgraph
Dataset
(mimic iv demo dataset)[https://physionet.org/content/mimic-iv-demo/2.2/]
Generate main dataset from compressed files
python3 -m deepehrgraph.dataset.dataset_generator
This step will download the archive files from physionet and generate the master dataset in the data
folder.
CCI and ECI indexes are calculated and added to the dataset.
Features
In the context of medical studies, CCI (Charlson Comorbidity Index) and ECI (Elixhauser Comorbidity Index) are tools used to assess the burden of comorbidities in individuals. Comorbidities refer to the presence of additional health conditions in a patient alongside the primary condition under investigation. Both CCI and ECI are designed to quantify and summarize the impact of comorbidities on patient health.
Charlson Comorbidity Index (CCI):
Purpose: Developed by Dr. Mary Charlson, the CCI is a widely used tool to predict the 10-year mortality for patients with multiple comorbidities. It assigns weights to various comorbid conditions based on their impact on mortality. Calculation: Each comorbid condition is assigned a score, and the total CCI score is the sum of these individual scores. The higher the CCI score, the greater the burden of comorbidities. Conditions: The CCI includes conditions such as myocardial infarction, heart failure, dementia, diabetes, liver disease, and others.
Elixhauser Comorbidity Index (ECI):
Purpose: The ECI, developed by Dr. Claudia Elixhauser, is another comorbidity index used to assess the impact of comorbid conditions on healthcare outcomes. It is often employed in administrative databases and research studies. Calculation: Similar to the CCI, the ECI assigns weights to comorbid conditions. However, the ECI covers a broader range of conditions and is often used for risk adjustment in research studies. Conditions: The ECI includes a comprehensive list of conditions such as hypertension, obesity, renal failure, coagulopathy, and others.
Selected features:
['gender', 'age', 'n_ed_30d', 'n_ed_90d', 'n_ed_365d', 'n_hosp_30d',
'n_hosp_90d', 'n_hosp_365d', 'n_icu_30d', 'n_icu_90d', 'n_icu_365d',
'cci_MI', 'cci_CHF', 'cci_PVD', 'cci_Stroke', 'cci_Dementia',
'cci_Pulmonary', 'cci_Rheumatic', 'cci_PUD', 'cci_Liver1', 'cci_DM1',
'cci_DM2', 'cci_Paralysis', 'cci_Renal', 'cci_Cancer1', 'cci_Liver2',
'cci_Cancer2', 'cci_HIV', 'eci_CHF', 'eci_Arrhythmia', 'eci_Valvular',
'eci_PHTN', 'eci_PVD', 'eci_HTN1', 'eci_HTN2', 'eci_Paralysis',
'eci_NeuroOther', 'eci_Pulmonary', 'eci_DM1', 'eci_DM2',
'eci_Hypothyroid', 'eci_Renal', 'eci_Liver', 'eci_PUD', 'eci_HIV',
'eci_Lymphoma', 'eci_Tumor2', 'eci_Tumor1', 'eci_Rheumatic',
'eci_Coagulopathy', 'eci_Obesity', 'eci_WeightLoss', 'eci_FluidsLytes',
'eci_BloodLoss', 'eci_Anemia', 'eci_Alcohol', 'eci_Drugs',
'eci_Psychoses', 'eci_Depression']
Outcomes
Data preprocessing
Feature selection
Use Case
Models
Resources
https://mimic.mit.edu/docs/iv/modules/hosp/ Xie F, Zhou J, Lee JW, Tan M, Li SQ, Rajnthern L, Chee ML, Chakraborty B, Wong AKI, Dagan A, Ong MEH, Gao F, Liu N. Benchmarking emergency department prediction models with machine learning and public electronic health records. Scientific Data 2022 Oct; 9: 658. https://doi.org/10.1038/s41597-022-01782-9 https://www.sciencedirect.com/science/article/pii/S2352914823001089 https://github.com/healthylaife/MIMIC-IV-Data-Pipeline#How-to-use-the-pipeline
###ML https://scikit-learn.org/stable/common_pitfalls.html
Contributing
Install dependencies
pip3 install poetry
poetry install
Pre-commit hooks
poetry run pre-commit install
Run pre-commit hooks on all files
poetry run pre-commit run --all-files
Run tests
Tox is using pre-commit hooks to run tests and linting.
cd deep-ehr-graph
tox .
Project details
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Source Distribution
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