• Corpus ID: 219633780

First Steps Towards Patient-Friendly Presentation of Dutch Radiology Reports

@inproceedings{Dercksen2020FirstST,
  title={First Steps Towards Patient-Friendly Presentation of Dutch Radiology Reports},
  author={Koen Dercksen and Arjen P. de Vries},
  booktitle={SIIRH@ECIR},
  year={2020}
}
Nowadays, clinical patients are often free to access their own electronic health records (EHRs) online. Medical records are however not written with the patient in mind – the medical terminology necessary to ensure unambiguous communication between medical professionals on likelihood of pathology renders the EHRs less accessible to patients. By annotating these texts with links to external knowledge bases, the patients can be provided with additional reliable information to clarify terminology… 

Figures from this paper

Report of the First International Workshop on Semantic Indexing and Information Retrieval for Health from heterogeneous content types and languages

This article briefly summarizes the talks and discussions that occurred during the first edition of the International Workshop on Semantic Indexing and Information Retrieval for Health from

References

SHOWING 1-10 OF 27 REFERENCES

SNOMED-CT: The advanced terminology and coding system for eHealth.

Use of a Clinical Terminology, implemented within a clinical information system, will enable the delivery of many patient health benefits including electronic clinical decision support, disease screening and enhanced patient safety.

Learning to Summarize Radiology Findings

This work proposes to automate the generation of radiology impressions with neural sequence-to-sequence learning and proposes a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process.

Generating links to background knowledge: a case study using narrative radiology reports

A sequential labeling approach with syntactic features for anchor text identification in order to exploit syntactic regularities in medical terminology and combines this with a sub-anchor based approach to target finding, which is aimed at coping with the complex semantic structure of medical phrases.

Temporal Annotation in the Clinical Domain

The implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus, is discussed and a new annotation guideline has been developed, “the THyME Guidelines to ISO- timeML (THYME-Time ML)”.

Research Paper: A General Natural-language Text Processor for Clinical Radiology

Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms, using radiology as the test domain.

QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction

The proposed method achieves similar precision and recall of state-of-the-art systems on two clinical notes corpora, and outperforms MetaMap and cTAKES on a dataset of consumer drug reviews up to 135 times faster than both systems.

Clustering Large-scale Diverse Electronic Medical Records to Aid Annotation for Generic Named Entity Recognition

It is proposed that clustering clinical text documents is an effective way to aid the annotation effort and ensure coverage and the characteristics of clusters generated from a diverse dataset are examined.

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks, which showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations.

A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction

This study establishes a new state-of-the-art result for the task of CONTAINS temporal relation extraction, by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.

Entity linking for biomedical literature

A novel unsupervised collective inference approach is proposed to address the EL problem in a new domain and is able to outperform a current state-of-the-art supervised approach that has been trained with a large amount of manually labeled data.