NTCIR-16 Real-MedNLP

With the advent of the internet age, medical records are increasingly being written in electronic formats in place of paper, which leads to a higher importance of information processing techniques in medical fields. However, the amount of privacy-free medical text data is still small in non-English languages, such as Japanese and Chinese. In such a situation, we had proposed a series of previous four medical natural language processing (MedNLP) tasks, MedNLP-1, MedNLP-2, MedNLPDoc, and MedWeb. This task will yield promising technologies to develop practical computational systems for supporting a wide range of medical services.

NEWS

8.29 MedTxt-CRコーパスのサンプルデータを公開しました MedTxt-CR Corpus sample data is available
8.21 MedTxt-RRコーパスのサンプルデータを公開しました MedTxt-RR Corpus sample data is available
7.1 スケジュールを更新しました The schedule has been fixed
6.11 日本語版ホームページを公開しました
6.18 Our English website is available

About this workshop

Real-MedNLP is a shared task workshop for medical language processing using actual medical documents (case reports and radiology reports). The goal of this task is to promote the development of practical systems that support various medical services.

The Real-MedNLP task has two corpus-based tracks (MedTxt-CR Track and MedTxt-RR Track), each with three subtasks.

Datasets

MedTxt-CR Corpus

This dataset comprises a set of open-access case reports available at CiNii. Typically, open-access case reports are biased in the reporting of patients and diseases, due to differences in the policies various medical societies have towards open-access publications.

To reduce such bias caused by each medical society’s publication policy, we select case reports based on actual frequencies of patients and diseases.

  • Training set: 100 reports
  • Test set: 100 reports

 

A case report is detailed descriptions of a patient’s medical condition for research purposes. They track the onset and temporal progression of the patient’s disease and are large in quantity as medical societies typically have dedicated submission tracks for consolidating these reports. Considering these advantages, case reports possess enormous potential as a rich source of information. Furthermore, the format of a case report is similar to that of a discharge summary, which is frequently used in healthcare contexts. Techniques developed here for case report analysis could also be applied to analyze discharge summaries.

An example of a case report with named entities recognized

  NEW 8.29 UPDATED

MedTxt-RR Corpus

This dataset comprises a set of 15 cases, in which 9 different radiologists describe the findings for each report. In total, 135 texts will be made available.

  • Training set: 72 texts
  • Test set: 63 texts

 

A radiology report is a type of clinical document that is written by a radiologist. Basically, they focus on a single radiology image and describes all potential findings (including potential diseases) that can be expected from the image. While reports and target images are paired, most research on radiology reports tends to focus only on images, due to the hype surrounding image-based AI  (such as automatic diagnosis of X-rays, CT, and MRI). One of the biggest problems when handling radiology reports is in the variety of writing styles. Although a diagnosis can be written in a variety of ways (diversity of expression), conventionally, only one report is created per image. As such, simply collecting reports from medical institutions may not yield enough information on the variability in reporting styles for the same diagnosis. Consequently, we included independent reports from multiple doctors for the same CT image.

An example of a radiology report with named entities recognized

  NEW 8.21 UPDATED

Task Overview

Few-resource Named Entity Recognition (NER)

Since NER is arguably the most fundamental information extraction problem for MedNLP, we designed challenges pertaining to NER on our real-world clinical documents, with a sample of only 100-200 documents. This scale of corpus size tends to be regarded as a “few-resource machine learning”, which is a de-facto standard for any sort of MedNLP in general.

Subtask 1: Just 100 Training

  • NER using the training set consisting of 100 documents
  • This subtask is equivalent to standard supervised learning with few resources

Subtask 2: Guideline Learning

  • NER using the example text for each tag in Annotation Guideline
  • This simulates the training of human annotators, who often learn from the annotation guidelines provided by researchers.
<article id="JP0217-29" title="著明な好酸球増多を伴った非昏睡型急性肝不全の一例"> 
Case Study: <TIMEX3 type="AGE">53 year old</TIMEX3> female patient.
Chief Complaint: <d certainty="positive">Fever</d>.
Progress: Patient was <cc state="executed">seen</cc> at the dermatology department of our hospital <TIMEX3 type="DATE">2 years before 20XX</TIMEX3> and presented a <d certainty="positive">skin rash</d> that the diagnosis identified as <d certainty="positive">bullous pemphigoid</d>.
<m-key state="executed">Prednisolone (PSL)</m-key> <m-val>1 mg/kg/day</m-val> was introduced and the patient <TIMEX3 type="TIME">was</TIMEX3> managed with concomitant <m-key state="executed">immunomodulators</m-key> with <c>a gradual decrease</c> in the <m-key state="executed">PSL</m-key> level.
<m-key state="negated">PSL</m-key> was voluntarily discontinued in <TIMEX3 type="DATE">August, 20XX</TIMEX3> when the <m-key state="executed">PSL</m-key> dosage had been <c>reduced</c> to <m-val>6 mg/day</m-val>, but there was no <d certainty="negative">worsening of the skin rash</d>.
...(skipped)...
</article>

Application

Subtask 3:Application

[MedTxt-CR Track] Adverse Drug Event detection (ADE)

Extract adverse drug event (ADE) information from case reports and create a table

Task image of Subtask 3 ADE
[MedTxt-RR Track] Case Identification (CI)

Identify the radiology report for the same case

Schedule

  • September 30, 2021: Datasets release
  • September 1 December 1, 2021: Registration Due
  • December 2021-February 2022 January 2022: Formal Run
  • February 1, 2022: Evaluation Result Release
  • February 1, 2022: Draft Task Overview Paper Release
  • March 1, 2022: Draft Participant Paper Submission Due
  • May 1, 2022: All Camera-ready Paper Submission Due
  • June 14-17, 2022: NTCIR-16 Conference (NII, Tokyo, Japan) (An option for online presentation will be available)

Organizer

  • Eiji Aramaki (Nara Institute of Science and Technology, Japan)
  • Shoko Wakamiya (Nara Institute of Science and Technology, Japan)
  • Shuntaro Yada (Nara Institute of Science and Technology, Japan)
  • Yuta Nakamura (The University of Tokyo, Japan)

Collaborators

Inquiry