"Nothing Abnormal": Disambiguating Medical Reports via Contrastive Knowledge Infusion

by   Zexue He, et al.

Sharing medical reports is essential for patient-centered care. A recent line of work has focused on automatically generating reports with NLP methods. However, different audiences have different purposes when writing/reading medical reports – for example, healthcare professionals care more about pathology, whereas patients are more concerned with the diagnosis ("Is there any abnormality?"). The expectation gap results in a common situation where patients find their medical reports to be ambiguous and therefore unsure about the next steps. In this work, we explore the audience expectation gap in healthcare and summarize common ambiguities that lead patients to be confused about their diagnosis into three categories: medical jargon, contradictory findings, and misleading grammatical errors. Based on our analysis, we define a disambiguation rewriting task to regenerate an input to be unambiguous while preserving information about the original content. We further propose a rewriting algorithm based on contrastive pretraining and perturbation-based rewriting. In addition, we create two datasets, OpenI-Annotated based on chest reports and VA-Annotated based on general medical reports, with available binary labels for ambiguity and abnormality presence annotated by radiology specialists. Experimental results on these datasets show that our proposed algorithm effectively rewrites input sentences in a less ambiguous way with high content fidelity. Our code and annotated data are released to facilitate future research.


page 1

page 2

page 3

page 4


Identifying Harm Events in Clinical Care through Medical Narratives

Preventable medical errors are estimated to be among the leading causes ...

Language models are susceptible to incorrect patient self-diagnosis in medical applications

Large language models (LLMs) are becoming increasingly relevant as a pot...

Modeling Mobile Visualization for Medical Reports of Complex Chronic Diseases

Visualizing medical histories of patients with complex chronic diseases ...

Learning Semi-Structured Representations of Radiology Reports

Beyond their primary diagnostic purpose, radiology reports have been an ...

Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training

When reading images, radiologists generate text reports describing the f...

Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation

Medical report generation is a challenging task since it is time-consumi...

Intimate Partner Violence and Injury Prediction From Radiology Reports

Intimate partner violence (IPV) is an urgent, prevalent, and under-detec...

Please sign up or login with your details

Forgot password? Click here to reset