https://github.com/callahantiff/pheknowvec
Translational Computational Phenotyping
https://github.com/callahantiff/pheknowvec
abra-collaboratory computational-phenotyping omop open-biomedical-ontologies phenotyping
Last synced: about 1 month ago
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Translational Computational Phenotyping
- Host: GitHub
- URL: https://github.com/callahantiff/pheknowvec
- Owner: callahantiff
- Created: 2019-03-06T04:03:13.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-22T22:19:00.000Z (almost 2 years ago)
- Last Synced: 2025-02-14T21:55:31.747Z (3 months ago)
- Topics: abra-collaboratory, computational-phenotyping, omop, open-biomedical-ontologies, phenotyping
- Language: Python
- Homepage:
- Size: 17.7 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
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README
## PheKnowVec
PheKnowVec is a novel method for deriving, implementing, and validating computational phenotypes. PheKnowVec leverages standardized clinical terminologies and open biomedical ontologies to derive, implement, and validate computational phenotype definitions in a scalable embedded structure.
Please see the [Project Wiki](https://github.com/callahantiff/PheKnowVec/wiki) for more information!
#### This is a Reproducible Research Repository
This repository contains more than just code, it provides a detailed and transparent narrative of our research process. For detailed information on how we use GitHub as a reproducible research platform, click [here](https://github.com/callahantiff/PheKnowVec/wiki/Using-GitHub-as-a-Reproducible-Research-Platform).
**Preliminary results were presented at the 2020 Joint Meeting of the American Medical Informatics Association:**
Callahan TJ, Wyrwa J, Trinkley KE, Hunter LE, Kahn MG, Bennett TD. (2020, March). Towards Automating Computational Phenotyping: Exploring the Trade-offs of Different Vocabulary Mapping Strategies. Talk; Informatics Summits of the American Medical Informatics Association, Houston, TX; [Podium Abstract](https://www.dropbox.com/s/mccv9b10m4arvt3/2020%20AMIA%20Informatics%20Summit%20-%20Revision.pdf?dl=1)
______
### Getting Started
**Dependencies**
This repository is built using Python 3.6.2. To install the libraries used in this repository, run the line of code shown below from the within the project directory.
```
pip install -r requirements.txt
```
**Data**
This code assumes that input data is stored in a GoogleSheet, thus this repository contains code which relies on
Google's [DriveAPI](https://developers.google.com/drive/) and
[SheetsAPI](https://developers.google.com/sheets/api/). In order to use this functionality you will need to:
- Complete the steps described [here](https://github.com/burnash/gspread)
- Save the json file containing your credentials to `./resources/programming/Google_API/`
- Rename the credential file to "secret_client_gs.json"
This code assumes that your input Google Sheet will follow a specific format:
Phenotype | Cohort | Criteria | Phenotype_Criteria | Input_Type | Source_Domain | Source_Vocabulary | Source_Code | Source_Label
-- | -- | -- | -- | -- | -- | -- | -- | --
ADHD | Case | Include | Presence of at least 1 relevant code in >1 in-person visits, on separate calendar days | Code | Condition | ICD9CM | '314.0' | Attention deficit disorder of childhood
ADHD | Case | Include | Presence of >1 prescriptions of ADHD-related medications | String | Drug | None | '%adderall%' | adderall
**SQL Queries**
- This project assumes that you will want to use the SQL queries that we have prepared and store as GitHub Gist.
There are two types of queries run:
1. Queries to map code sets
2. Queries to create patient cohorts