Evidence map›Paper›PMID 37046469›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models.

Max Tigo Rietberg, Van Bach Nguyen, Jeroen Geerdink, Onno Vijlbrief, Christin Seifert

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. The DRAGON benchmark for clinical NLP.NPJ digital medicine · 2025
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Max Tigo RietbergFaculty of EEMCS, University of Twente, 7500 AE Enschede, The Netherlands.ORCID 0000-0001-8959-6477
Van Bach NguyenInstitute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany.ORCID 0000-0002-4576-9302
Jeroen GeerdinkHospital Group Twente (ZGT), 7555 DL Hengelo, The Netherlands.ORCID 0000-0001-6718-6653
Onno VijlbriefHospital Group Twente (ZGT), 7555 DL Hengelo, The Netherlands.ORCID 0000-0003-2303-6009
Christin SeifertInstitute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany.ORCID 0000-0002-6776-3868

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.

Indexed as

BERThealth informaticsnatural language processingtext classification

Identifiers

PMID37046469
PMCPMC10093295

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.