| Name | equiflow JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | equiflow is a package to generate equity-focused cohort selection flow diagrams. |
| upload_time | 2024-07-13 02:40:54 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 João Matos Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
equiflow
clinical research
cohort
equity
flow diagram
machine learning
statistics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# equiflow
***Under construction!***
*equiflow* is a package designed to generate "Equity-focused Cohort Selection Flow Diagrams". We hope to facilitate research, increase its reproducibility, and improve the transparency of the process of cohort curation in machine learning studies.
## Vision
*equiflow* will provide tabular and visual representations of inclusion and exclusion criteria applied to a clinical dataset. Each patient exclusion step can depict the cohort composition across demographics and outcomes, to interrogate potential sampling selection bias.
This package is designed to enhance the transparency and reproducibility of research in the medical machine learning field. It complements other tools like tableone, which is used for generating summary statistics for patient populations.
## Citation
The concept was first introuced in our [position paper](https://www.sciencedirect.com/science/article/pii/S1532046424000492).
> Ellen JG, Matos J, Viola M, et al. Participant flow diagrams for health equity in AI. J Biomed Inform. 2024;152:104631. [https://doi.org/10.1016/j.jbi.2024.104631](https://doi.org/10.1016/j.jbi.2024.104631)
## Motivation
Selection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the “Data Cards” initiative for transparency in AI research, we advocate for the addition of a **participant flow diagram for AI studies detailing relevant sociodemographic** and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms.
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"description": "# equiflow\n\n***Under construction!***\n\n*equiflow* is a package designed to generate \"Equity-focused Cohort Selection Flow Diagrams\". We hope to facilitate research, increase its reproducibility, and improve the transparency of the process of cohort curation in machine learning studies.\n\n\n## Vision\n*equiflow* will provide tabular and visual representations of inclusion and exclusion criteria applied to a clinical dataset. Each patient exclusion step can depict the cohort composition across demographics and outcomes, to interrogate potential sampling selection bias.\n\nThis package is designed to enhance the transparency and reproducibility of research in the medical machine learning field. It complements other tools like tableone, which is used for generating summary statistics for patient populations.\n\n\n## Citation\nThe concept was first introuced in our [position paper](https://www.sciencedirect.com/science/article/pii/S1532046424000492).\n\n> Ellen JG, Matos J, Viola M, et al. Participant flow diagrams for health equity in AI. J Biomed Inform. 2024;152:104631. [https://doi.org/10.1016/j.jbi.2024.104631](https://doi.org/10.1016/j.jbi.2024.104631)\n\n\n## Motivation\n\nSelection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the \u201cData Cards\u201d initiative for transparency in AI research, we advocate for the addition of a **participant flow diagram for AI studies detailing relevant sociodemographic** and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms.\n\n",
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