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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2409.16603 |
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| _version_ | 1866912045631275008 |
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| author | Xu, Justin Chen, Zhihong Johnston, Andrew Blankemeier, Louis Varma, Maya Hom, Jason Collins, William J. Modi, Ankit Lloyd, Robert Hopkins, Benjamin Langlotz, Curtis Delbrouck, Jean-Benoit |
| author_facet | Xu, Justin Chen, Zhihong Johnston, Andrew Blankemeier, Louis Varma, Maya Hom, Jason Collins, William J. Modi, Ankit Lloyd, Robert Hopkins, Benjamin Langlotz, Curtis Delbrouck, Jean-Benoit |
| contents | Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ("Discharge Me!"). RRG24 involves generating the 'Findings' and 'Impression' sections of radiology reports given chest X-rays. "Discharge Me!" involves generating the 'Brief Hospital Course' and 'Discharge Instructions' sections of discharge summaries for patients admitted through the emergency department. "Discharge Me!" submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for "Discharge Me!". |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!" Xu, Justin Chen, Zhihong Johnston, Andrew Blankemeier, Louis Varma, Maya Hom, Jason Collins, William J. Modi, Ankit Lloyd, Robert Hopkins, Benjamin Langlotz, Curtis Delbrouck, Jean-Benoit Computation and Language Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ("Discharge Me!"). RRG24 involves generating the 'Findings' and 'Impression' sections of radiology reports given chest X-rays. "Discharge Me!" involves generating the 'Brief Hospital Course' and 'Discharge Instructions' sections of discharge summaries for patients admitted through the emergency department. "Discharge Me!" submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for "Discharge Me!". |
| title | Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!" |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.16603 |