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Added doctest helpers to namespace and used ruff for linting / pre-commit over other older tools. (#179)
* Added doctest helpers to namespace and used ruff for linting
* Use yaml instead of yml and remove unneeded exclusions from pre-commit config.
* Update ruff
* Adding back in erroneously deleted lines.
* Adding back in erroneously deleted lines.
* Correct admonition syntax
* Correcting numbering mismatch
* Removed more no-longer-needed imports in doctests
* Removed unnecessary variable definitions in doctests
* More small stylist cleanups for config doctests
* Removed DTZ001 lint rule and UTC stuff to avoid 3.10 version issue
* Removed unnecessary noqa comment
* Added coverage exclusion configs
-**\[2024-12-10\]** Latest `polars` version (`1.17.1`) is now supported.
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-**\[2024-10-28\]** Nested derived predicates and derived predicates between static variables and plain predicates can now be defined.
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-**\[2024-09-01\]** Predicates can now be defined in a configuration file separate to task criteria files.
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-**\[2024-08-29\]** Latest `MEDS` version (`0.3.3`) is now supported.
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-**\[2024-08-10\]** Expanded predicates configuration language to support regular expressions, multi-column constraints, and multi-value constraints.
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-**\[2024-07-30\]** Added ability to place constraints on static variables, such as patient demographics.
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-**\[2024-06-28\]** Paper available at [arXiv:2406.19653](https://arxiv.org/abs/2406.19653).
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-**[2025-01-22]** ACES accepted to ICLR'25!
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-**[2024-12-10]** Latest `polars` version (`1.17.1`) is now supported.
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-**[2024-10-28]** Nested derived predicates and derived predicates between static variables and plain predicates can now be defined.
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-**[2024-09-01]** Predicates can now be defined in a configuration file separate to task criteria files.
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-**[2024-08-29]** Latest `MEDS` version (`0.3.3`) is now supported.
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-**[2024-08-10]** Expanded predicates configuration language to support regular expressions, multi-column constraints, and multi-value constraints.
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-**[2024-07-30]** Added ability to place constraints on static variables, such as patient demographics.
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-**[2024-06-28]** Paper available at [arXiv:2406.19653](https://arxiv.org/abs/2406.19653).
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Automatic Cohort Extraction System (ACES) is a library that streamlines the extraction of task-specific cohorts from time series datasets formatted as event-streams, such as Electronic Health Records (EHR). ACES is designed to query these EHR datasets for valid subjects, guided by various constraints and requirements defined in a YAML task configuration file. This offers a powerful and user-friendly solution to researchers and developers. The use of a human-readable YAML configuration file also eliminates the need for users to be proficient in complex dataframe querying, making the extraction process accessible to a broader audience.
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@@ -60,7 +60,9 @@ Install with dependencies from the root directory of the cloned repo:
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pip install -e .
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```
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**Note**: To avoid potential dependency conflicts, please install ESGPT first before installing ACES. This ensures compatibility with the `polars` version required by ACES.
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> [!NOTE]
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> To avoid potential dependency conflicts, please install ESGPT first before installing ACES. This ensures
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> compatibility with the `polars` version required by ACES.
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## Instructions for Use
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- `value_max_inclusive` (optional): Must be a boolean specifying whether `value_max` is inclusive or not.
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- `other_cols` (optional): Must be a 1-to-1 dictionary of column name and column value, which places additional constraints on further columns.
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**Note**: For memory optimization, we strongly recommend using either the List of Values or Regular Expression formats whenever possible, especially when needing to match multiple values. Defining each code as an individual string will increase memory usage significantly, as each code generates a separate predicate column. Using a list or regex consolidates multiple matching codes under a single column, reducing the overall memory footprint.
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> [!NOTE]
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> For memory optimization, we strongly recommend using either the List of Values or Regular Expression formats
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> whenever possible, especially when needing to match multiple values. Defining each code as an individual
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> string will increase memory usage significantly, as each code generates a separate predicate column. Using a
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> list or regex consolidates multiple matching codes under a single column, reducing the overall memory
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