EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914368833191936 |
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| author | Lee, Gyubok Hwang, Hyeonji Bae, Seongsu Kwon, Yeonsu Shin, Woncheol Yang, Seongjun Seo, Minjoon Kim, Jong-Yeup Choi, Edward |
| author_facet | Lee, Gyubok Hwang, Hyeonji Bae, Seongsu Kwon, Yeonsu Shin, Woncheol Yang, Seongjun Seo, Minjoon Kim, Jong-Yeup Choi, Edward |
| contents | We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insurance review and health records teams. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and used the responses to create seed questions. We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll. Our dataset poses a unique set of challenges: the model needs to 1) generate SQL queries that reflect a wide range of needs in the hospital, including simple retrieval and complex operations such as calculating survival rate, 2) understand various time expressions to answer time-sensitive questions in healthcare, and 3) distinguish whether a given question is answerable or unanswerable. We believe our dataset, EHRSQL, can serve as a practical benchmark for developing and assessing QA models on structured EHR data and take a step further towards bridging the gap between text-to-SQL research and its real-life deployment in healthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_07695 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records Lee, Gyubok Hwang, Hyeonji Bae, Seongsu Kwon, Yeonsu Shin, Woncheol Yang, Seongjun Seo, Minjoon Kim, Jong-Yeup Choi, Edward Computation and Language Artificial Intelligence We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insurance review and health records teams. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and used the responses to create seed questions. We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll. Our dataset poses a unique set of challenges: the model needs to 1) generate SQL queries that reflect a wide range of needs in the hospital, including simple retrieval and complex operations such as calculating survival rate, 2) understand various time expressions to answer time-sensitive questions in healthcare, and 3) distinguish whether a given question is answerable or unanswerable. We believe our dataset, EHRSQL, can serve as a practical benchmark for developing and assessing QA models on structured EHR data and take a step further towards bridging the gap between text-to-SQL research and its real-life deployment in healthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL. |
| title | EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2301.07695 |