Ankit Modi
2024
Overview of the First Shared Task on Clinical Text Generation: RRG24 and “Discharge Me!”
Justin Xu
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Zhihong Chen
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Andrew Johnston
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Louis Blankemeier
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Maya Varma
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Jason Hom
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William J. Collins
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Ankit Modi
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Robert Lloyd
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Benjamin Hopkins
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Curtis Langlotz
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Jean-Benoit Delbrouck
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
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!”.
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Co-authors
- Justin Xu 1
- Zhihong Chen 1
- Andrew Johnston 1
- Louis Blankemeier 1
- Maya Varma 1
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