Datasets:

aai530-group6
/

ddxplus-french

@@ -103,7 +103,6 @@ The file `release_conditions.json` contains information about the pathologies th
    - **symptoms**: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding `name` entry in the  `release_evidences.json` file.
    - **antecedents**: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding `name` entry in the  `release_evidences.json` file.
 ## Patient Description
 Each patient in each of the 3 sets has the following attributes:
@@ -114,7 +113,6 @@ Each patient in each of the 3 sets has the following attributes:
    - **INITIAL_EVIDENCE**: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., `EVIDENCES`) and it is part of this list.
    - **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.
 ## Note:
 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.

    - **symptoms**: data structure describing the set of symptoms characterizing the pathology. Each symptom is represented by its corresponding `name` entry in the  `release_evidences.json` file.
    - **antecedents**: data structure describing the set of antecedents characterizing the pathology. Each antecedent is represented by its corresponding `name` entry in the  `release_evidences.json` file.
 ## Patient Description
 Each patient in each of the 3 sets has the following attributes:
    - **INITIAL_EVIDENCE**: the evidence provided by the patient to kick-start an interaction with an ASD/AD system. This is useful during model evaluation for a fair comparison of ASD/AD systems as they will all begin an interaction with a given patient from the same starting point. The initial evidence is randomly selected from the binary evidences found in the evidence list mentioned above (i.e., `EVIDENCES`) and it is part of this list.
    - **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.
 ## Note:
 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.