Titlebook: Health Information Processing. Evaluation Track Papers; 8th China Conference Buzhou Tang,Qingcai Chen,Hui Zong Conference proceedings 2023

modest

Health Information Processing. Evaluation Track Papers8th China Conference

Graves’-disease

CHIP2022 Shared Task Overview: Medical Causal Entity Relationship Extractionrectly label these correct reasoning relationships and corresponding subject-object entities. A total of 49 teams submitted results for the preliminary round with the highest Macro-F1 value of 0.4510. A total of 25 teams submitted results for final round with the highest Macro-F1 value of 0.4416.

micronized

Research on Decision Tree Method of Medical Text Based on Information Extractionets (Text2MDT) The medical pre-training model allows the model to have a deeper under-standing of medical vocabulary and the dependencies between vocabulary; The triplet ex-traction method uses biaffine to judge that the entity relationship is suitable for the triplet extraction of the evaluation da

BUDGE

thrombosis

TripleMIE: Multi-modal and Multi Architecture Information Extractionf target fields..To achieve the above goals, we propose a knowledge-based multi-modal and multi-architecture medical voucher information extraction method, namely TripleMIE, which includes I2SM: Image to sequence model, L-SPN: Large scale PLM-based span prediction net, MMIE: multi-modal information