The article proposes a novel approach to medical report generation, leveraging the Incremental Transformer with Deliberation Decoder (ITDD) architecture. The ITDD framework enhances the collaboration between distinct paragraphs during the generation process, ensuring improved overall quality. By introducing a multi-generator deliberation framework, the authors aim to facilitate paragraph generation collaboration among multiple generators via message passing and deliberation polish.
Drafting Medical Reports
The proposed approach expands the application scenarios of traditional EHR synthesis by generating medical reports that cover various tasks beyond limited medical event labels. These tasks include symptom extraction, diagnosis prediction, and clinical outcome prediction. The authors demonstrate the effectiveness of their approach through an example of a single-visit EHR data, showcasing three types of medical events on the left and corresponding detailed medical report paragraphs on the right.
Message Passing and Deliberation
The ITDD framework comprises two phases: draft and polish. In the draft phase, multiple generators sequentially draft three paragraphs through forward message passing. Each generator uses the patient’s health state as a conditional signal to control the generation process. During the polish phase, the authors use the drafted paragraphs as deliberation feedback to refine the remaining two paragraphs. This collaboration between distinct paragraphs enhances overall generation quality and aligns better with medical events.
Multi-Generator Deliberation
The proposed approach introduces a multi-generator deliberation framework to facilitate paragraph generation collaboration among multiple generators. Each generator (symptom, diagnosis, and medication report) collaborates via message passing and deliberation polish. By engaging in this collaboration, the authors improve the coherence and consistency of generated reports.
Conclusion
In conclusion, the article presents an incremental transformer with deliberation decoder (ITDD) architecture for document grounded conversations. The proposed approach enhances medical report generation by incorporating multiple generators that collaborate via message passing and deliberation polish. By demystifying complex concepts through everyday language and engaging analogies, the authors provide a comprehensive summary of their work without oversimplifying the essence of the article.
