2023-11-03 オークリッジ国立研究所(ORNL)
◆研究者は、バイオメディカル機械読解理解(bio-MRC)をCPGに適用し、新しい分野を開拓しました。彼らは独自のデータセットを作成し、トレーニングに転送学習を使用し、モデルを開発しました。
◆このモデルは、質問の約78%に正確な一致回答を提供し、医療提供者が迅速に情報を取得できる可能性を示唆しています。将来的には、表や図から情報を引き出すためのモデルの強化と、医療技術の採用に対処する計画です。
<関連情報>
- https://www.ornl.gov/news/machine-learning-brings-faster-answers-healthcare-providers
- https://ieeexplore.ieee.org/document/10011422
cpgQA: 臨床診療ガイドラインの機械読解タスクのベンチマークデータセットと転移学習を用いたケーススタディ cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning
Maria Mahbub,Edmon Begoli,Susana Martins,Alina Peluso,Suzanne Tamang,Gregory Peterson
Institute of Electrical and Electronics Engineers Xplore Published:09 January 2023
DOI:https://doi.org/10.1109/ACCESS.2023.3235265
Abstract
Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing, is a vital application of a computer-assisted clinical decision support system. It can help clinicians extract critical information effortlessly for clinical decision-making by comprehending and answering questions from biomedical text data. While recent advances in bio-MRC consider text data from resources such as clinical notes and scholarly articles, the clinical practice guidelines (CPGs) are still unexplored in this regard. CPGs are a pivotal component of clinical decision-making at the point of care as they provide recommendations for patient care based on the most up-to-date information available. Although CPGs are inherently terse compared to a multitude of articles, often, clinicians find them lengthy and complicated to use. In this paper, we define a new problem domain – bio-MRC on CPGs – where the ultimate goal is to assist clinicians in efficiently interpreting the clinical practice guidelines using MRC systems. To that end, we develop a manually annotated and subject-matter expert-validated benchmark dataset for the bio-MRC task on CPGs – cpgQA. This dataset aims to evaluate intelligent systems performing MRC tasks on CPGs. Hence, we employ the state-of-the-art MRC models to present a case study illustrating an extensive evaluation of the proposed dataset. We address the problem of lack of training data in this newly defined domain by applying transfer learning. The results show that while the current state-of-the-art models perform well with 78% exact match scores on the dataset, there is still room for improvement, warranting further research on this problem domain. We release the dataset at https://github.com/mmahbub/cpgQA .
An example context and question-answer pair from the benchmark dataset for machine reading comprehension tasks on clinical practice guidelines – cpgQA. cpgQA was built ma…View more
