deepehrgraph 0.3.3

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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 .