https://github.com/blainerothrock/cord19-risk-factors
https://github.com/blainerothrock/cord19-risk-factors
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/blainerothrock/cord19-risk-factors
- Owner: blainerothrock
- Created: 2020-05-16T19:34:47.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-10T18:00:52.000Z (about 5 years ago)
- Last Synced: 2025-02-08T18:30:42.109Z (4 months ago)
- Language: Jupyter Notebook
- Size: 103 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# COVID-19 Risk Factor Analysis
## Summary
Using the [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks)
dataset, we plan to use NLP/NLU methods to identify risk factors for COVID-19. Time permitting,
we will compare risk factors discussed in research with those mentioned
in the main-stream media.
We will make 3 passes through the corpus:
1. Train a language model on the CORD-19 research papers.
2. Store the `h_t` (hidden state/prediction pt of last layer) for each manually defined risk factor (below)
like so: `{'Diabetes': [.342, ..., -.123], 'Smoking': [0.323, ..., -0.432]}`
3. Compare all `h_t`'s in the data with our stored set of `h_t`'s
using some similarity metric in order to try to identify new risk factors.
Between steps 2-3, we might do clustering analysis on the stored
`h_t`'s to see how well they group. Can also PCA down to 2 dims
and visualize.
## Manually Identified Risk Factors
[CDC Guidance on Risk Factors](https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-at-higher-risk.html)
* 65 years and older
* Living in a nursing home or long-term care facility
* Chronic lung disease or moderate to severe asthma
* Immunocomprimised:
- Cancer treatment
- Smoking
- Bone marrow transplantation
- Immune deficiencies
- Pooly controlled HIV or AIDS
- Prolonged use of corticosteroids
- "Other immune weaking medication"
* Severe obesity (BMI >= 40)
* Diabetes
* Chronic kidney disease undergoing dialysis
* Liver disease
* Hemoglobin disorders
- Sickle cell disease
- Thalassemia
* Serious heart conditions
- Heart failure
- Coronary artery disease
- Congenital heart disease
- Cardiomyopathies
- Pulmonary hypertension
### Setting up sqlite
[csv to sqlite3](https://www.sqlitetutorial.net/sqlite-import-csv/)