Automated Medical Reporting: From Multimodal Inputs to Medical Reports through Knowledge Graphs

Maas, Lientje; Kisjes, Adriaan; Hashemi, Iman; Heijmans, Floris; Dalpiaz, Fabiano; Dulmen, Sandra van; Brinkkemper, Sjaak

doi:10.5220/0010261605090514

Cited by 2 publications

(1 citation statement)

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“…To cope with the high workload due to the time required for proper documentation, Maas et al (2021) presented a real-time automated report of the interaction between care provider and patient, taking multimodal inputs that include audio, video, and sensor data from medical consultations, and in-troducing knowledge graphs -the Patient Medical Graph. They used speech and action recognition technology to first transform multimodal inputs into text before formally representing them and generating reports.…”

Section: Data-to-textmentioning

confidence: 99%

A Survey of Evaluation Methods of Generated Medical Textual Reports

Zhou¹,

Ringeval²,

Portet³

2023

Proceedings of the 5th Clinical Natural Language Processing Workshop

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Medical Report Generation (MRG) is a subtask of Natural Language Generation (NLG) and aims to present information from various sources in textual form and synthesize salient information, with the goal of reducing the time spent by domain experts in writing medical reports and providing support information for decision-making. Given the specificity of the medical domain, the evaluation of automatically generated medical reports is of paramount importance to the validity of these systems. Therefore, in this paper, we focus on the evaluation of automatically generated medical reports from the perspective of automatic and human evaluation. We present evaluation methods for general NLG evaluation and how they have been applied to domain-specific medical tasks. The study shows that MRG evaluation methods are very diverse, and that further work is needed to build shared evaluation methods. The state of the art also emphasizes that such an evaluation must be task specific and include human assessments, requesting the participation of experts in the field.

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Section: Data-to-textmentioning

confidence: 99%