Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
This repository contains the code of the following paper:
@misc{ziletti2024retrieval,
title={Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records},
author={Angelo Ziletti and Leonardo D'Ambrosi},
year={2024},
eprint={2403.09226},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Please find the Preprint here: Ziletti and D'Ambrosi, https://arxiv.org/abs/2403.09226
This paper is accepted at NAACL 2024 Clinical NLP Workshop (https://clinical-nlp.github.io/2024/)
Please cite this work if you use this code in your work or research.
Here is a summary workflow of the procedure:
and here are the main results:
Please refer to the manuscript for more details: https://arxiv.org/abs/2403.09226
The labelled question-SQL pairs are in the file text2sql_epi_dataset_omop.xlsx in the dataset folder.
We used Python 3.11 in our experiments.
Install latest version from the master branch on GitHub by:
git clone <GITHUB-URL>
cd text-to-sql-epi-ehr-naacl2024
pip install -r requirements.txt
First, we need to create a Pickle file (the query library) from the Excel file provided in the dataset folder
cd scripts
python run_querylib_calc.py
This Pickle file will be then used to perform the retrieval augmented generation (RAG) at the query generation stage.
To perform a prediction, run the script prediction_pipeline.py with your question in double quotes. For example,
cd scripts
python prediction_pipeline.py --question "How many women with atopic dermatitis?"
This should return:
Question: How many women with atopic dermatitis?
SQL template:
SELECT COUNT(DISTINCT p.person_id) AS female_patients_with_atopic_dermatitis
FROM condition_occurrence AS co
JOIN person AS p ON co.person_id = p.person_id
WHERE co.condition_concept_id IN ([condition@atopic dermatitis])
AND p.gender_concept_id = 8532;
To execute the query, you will Here is a link to the dataset on Google Cloud: https://console.cloud.google.com/marketplace/product/hhs/synpuf You can then open the link (will need a Google account), and run the query in your Google Account.
To make the query executable on Google BigQuery, you will need to modify the generated names of the table to comply with the Google Query Table Names. This is simply a renaming of the Tables.
For example,
SELECT COUNT(DISTINCT p.person_id) AS female_patients_with_atopic_dermatitis
FROM person AS p
JOIN condition_occurrence AS co ON p.person_id = co.person_id
JOIN concept AS c ON co.condition_concept_id = c.concept_id
WHERE p.gender_concept_id = 8532
AND c.concept_id IN (133834,4298597,4066382,4298599,4296193,4080929,4296192,4290738,4290734,4206125,4290736,4080928,4033671,4031630,4297478,4296190,4031631,4080927,4298598,4298601,4031013,4297362,4290740,4297495,40482226,4298600,4236759);
must be changed to
SELECT
COUNT(DISTINCT p.person_id) AS female_patients_with_atopic_dermatitis
FROM `bigquery-public-data.cms_synthetic_patient_data_omop.person` AS p
JOIN `bigquery-public-data.cms_synthetic_patient_data_omop.condition_occurrence` AS co ON p.person_id = co.person_id
JOIN `bigquery-public-data.cms_synthetic_patient_data_omop.concept` AS c ON co.condition_concept_id = c.concept_id
WHERE p.gender_concept_id = 8532
AND c.concept_id IN (133834,4298597,4066382,4298599,4296193,4080929,4296192,4290738,4290734,4206125,4290736,4080928,4033671,4031630,4297478,4296190,4031631,4080927,4298598,4298601,4031013,4297362,4290740,4297495,40482226,4298600,4236759);
to run on the BigQuery instance above.
If you have your own Snowflake Data Warehouse, you might not need to perform those changes.
In the repository, we provide a mockup version for medical coding to show how medical coding can be integrated in the process, as shown in the figure above. The coding is performed within the SNOMED ontology, since that is the underlying ontology for data stored in the OMOP common data model (OMOP-CDM). The table is usually called CONCEPT_TABLE in the OMOP-CDM.
We prepared a small mockup table in the dataset folder called medcodes_mockup.xlsx. Here we provide synthetic data following the data structure of the OMOP-CDM.
You can download the whole ontology from the National Library of Medicine here.
(Please note that you need to accept their terms and conditions)
More information on SNOMED-CT can be found for example here
First, we need to create a Pickle file (the medcodeonto library) from the Excel file provided
cd scripts
python run_medcoding_calc.py
This Pickle file will be then used to search for medical codes within the pickle file. Running the file will also provide an example of how coding is performed.
Note that this is only a mockup implementation. For a production-ready version, we suggest to use a vector database (for example qdrant), indexing the entire CONCEPT table for example from a Snowflake database.
After having run the Medical coding compilation step, you are ready to run the full pipeline: SQL query generation + medical coding.
This is an example:
cd scripts
python prediction_pipeline.py --med_coding True --question "How many females with atopic dermatitis"
This will return
Use medical coding: True
Use Snowflake database: False
No sentence-transformers model found with name BAAI/bge-large-en-v1.5. Creating a new one with MEAN pooling.
Question: How many females with atopic dermatitis
SQL template:
SELECT COUNT(DISTINCT p.person_id) AS female_patients_with_atopic_dermatitis
FROM condition_occurrence AS co
JOIN person AS p ON co.person_id = p.person_id
WHERE co.condition_concept_id IN ([condition@atopic dermatitis])
AND p.gender_concept_id = 8532;
Loading embedding from C:\Users\GKENY\OneDrive - Bayer\Personal Data\ascent\text-to-sql-epi-ehr-naacl2024\data_out\medcodes_onto.pkl
Retrieved codes: {'atopic dermatitis': [{'Score': 0.8221014142036438, 'CONCEPT_NAME': 'Dermatitis in children', 'CONCEPT_ID': 4296192, 'DOMAIN_ID': 'Condition', 'VOCABULARY_ID': 'SNOMED', 'STANDARD_CONCEPT': 'S', 'CONCEPT_CODE': 402196005}, {'Score': 0.8056577444076538, 'CONCEPT_NAME': 'Widespread dermatitis', 'CONCEPT_ID': 4298597, 'DOMAIN_ID': 'Condition', 'VOCABULARY_ID': 'SNOMED', 'STANDARD_CONCEPT': 'S', 'CONCEPT_CODE': 402186001}, {'Score': 0.7999188899993896, 'CONCEPT_NAME': 'Early childhood dermatitis', 'CONCEPT_ID': 4298599, 'DOMAIN_ID': 'Condition', 'VOCABULARY_ID': 'SNOMED', 'STANDARD_CONCEPT': 'S', 'CONCEPT_CODE': 402195009}, {'Score': 0.7992758750915527, 'CONCEPT_NAME': 'Facial dermatitis', 'CONCEPT_ID': 4298598, 'DOMAIN_ID': 'Condition', 'VOCABULARY_ID': 'SNOMED', 'STANDARD_CONCEPT': 'S', 'CONCEPT_CODE': 402192007}]}
Please note that the medical coding is based on a mockup ontology. Results will not be reliable
SQL filled:
SELECT COUNT(DISTINCT p.person_id) AS female_patients_with_atopic_dermatitis
FROM condition_occurrence AS co
JOIN person AS p ON co.person_id = p.person_id
WHERE co.condition_concept_id IN (4296192,4298597,4298599,4298598)
AND p.gender_concept_id = 8532;
Please note that the medical coding step is only exemplificatory here. To obtain reliable results, you will need to have access to the full SNOMED ontology (see Medical coding compilation section).
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