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@@ -34,7 +34,7 @@ load_dataset('thbndi/Mimic4Dataset', task, mimic_path=mimic_data, config_path=co
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  - "tensor": Represents each feature as an 2D array. There are separate arrays for labels, demographic data ('DEMO'), diagnosis ('COND'), medications ('MEDS'), procedures ('PROC'), chart/lab events ('CHART/LAB'), and output events data ('OUT'). Dynamic features are represented as 2D arrays where each row contains values at a specific time instant.
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  - "raw": Provide cohort from the pipeline without any encoding for custom data processing.
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  - "text": Represents diagnoses as text suitable for BERT or other similar text-based models.
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- For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file.
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  5. `generate_cohort` (bool) optionnal :
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  - Description: Determines whether to generate a new cohort from Mimic-IV data.
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  - Default: True
 
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  - "tensor": Represents each feature as an 2D array. There are separate arrays for labels, demographic data ('DEMO'), diagnosis ('COND'), medications ('MEDS'), procedures ('PROC'), chart/lab events ('CHART/LAB'), and output events data ('OUT'). Dynamic features are represented as 2D arrays where each row contains values at a specific time instant.
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  - "raw": Provide cohort from the pipeline without any encoding for custom data processing.
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  - "text": Represents diagnoses as text suitable for BERT or other similar text-based models.
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+ - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file.
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  5. `generate_cohort` (bool) optionnal :
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  - Description: Determines whether to generate a new cohort from Mimic-IV data.
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  - Default: True