Datasets:
Tasks:
Tabular Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
multi-class-classification
Languages:
French
Size:
1M - 10M
ArXiv:
Tags:
automatic-diagnosis
automatic-symptom-detection
differential-diagnosis
synthetic-patients
diseases
health-care
License:
Update README.md
Browse files
README.md
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@@ -3,6 +3,7 @@ language:
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- fr
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- en
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license: cc-by-4.0
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tags:
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- health
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- automatic-symptom-detection
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- machine-generated
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language_creators:
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- machine-generated
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pretty_name: DDXPlus
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size_categories:
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- 1K<n<10K
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source_datasets:
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- tabular-classification
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task_ids:
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- multi-class-classification
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paperswithcode_id: ddxplus
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configs:
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- config_name: default
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- **DIFFERENTIAL_DIAGNOSIS**: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form `[[patho_1, proba_1], [patho_2, proba_2], ...]` where `patho_i` is the pathology name (`condition_name` entry in the `release_conditions.json` file) and `proba_i` is its related probability.
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We hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.
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- fr
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- en
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license: cc-by-4.0
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license_link: https://creativecommons.org/licenses/by/4.0/
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tags:
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- health
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- automatic-symptom-detection
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- machine-generated
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language_creators:
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- machine-generated
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pretty_name: DDXPlus
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size_categories:
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- 1K<n<10K
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source_datasets:
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- tabular-classification
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task_ids:
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- multi-class-classification
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paperswithcode_id: ddxplus
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configs:
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- config_name: default
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- **DIFFERENTIAL_DIAGNOSIS**: The ground truth differential diagnosis for the patient. It is represented as a list of pairs of the form `[[patho_1, proba_1], [patho_2, proba_2], ...]` where `patho_i` is the pathology name (`condition_name` entry in the `release_conditions.json` file) and `proba_i` is its related probability.
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## Note:
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We hope this dataset will encourage future works for ASD and AD systems that consider the differential diagnosis and the severity of pathologies. It is important to keep in mind that this dataset is formed of synthetic patients and is meant for research purposes. Given the assumptions made during the generation process of this dataset, we would like to emphasize that the dataset should not be used to train and deploy a model prior to performing rigorous evaluations of the model performance and verifying that the system has proper coverage and representation of the population that it will interact with.
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