Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
sent2span: اكتشاف span لاستخراج بيكو في النص الطبي الطبيعي دون التوضيحات
275
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
إن النمو السريع في التجارب السريرية المنشورة يجعل من الصعب الحفاظ على مراجعات منهجية محدثة، والتي تتطلب إيجاد جميع التجارب ذات الصلة. وهذا يؤدي إلى اتخاذ القرارات السياسية والممارسة بناء على مجموعة فرعية خارجية وغير كاملة منحازة من الأدلة السريرية المتاحة. قد يكون استخراج ثم تطبيع السكان والتدخل والمقارنة والمقارنة والنتيجة (PICO) من مقالات التجريبية السريرية طريقة فعالة لتعيين التجارب التلقائية تلقائيا لمراجعات منهجية وتجنب البحث والفحص --- أكثر عمليات المراجعة المنهجية التي تستغرق وقتا طويلا. نقترح واختبار نهج رواية للكشف عن SPAN. الفرق الرئيسي بين أسلوبنا المقترح والنهج السابقة يأتي من اكتشاف المواقف دون الحاجة إلى بيانات تمدد المشروح واستخدام التعليقات التوضيحية على مستوى الجملة الجماعي فقط. تظهر التجارب على مجموعة بيانات اثنين من نتائج اكتشاف PICO Span تحقيق نتائج أعلى بكثير للتذكر عند مقارنتها بالأساليب الإشرافية بالكامل مع اكتشاف جملة بيكو على الأقل جيدة مثل التوضيح البشرية. من خلال إزالة الاعتماد على التعليقات التوضيحية للخبراء للكشف عن SPAN، يمكن استخدام هذا العمل في خط أنابيب رشاشيا لتحويل التوضيحات بيكو ذات الجودة المنخفضة ذات المستوى الخشفي والجمدية إلى معلومات منظمة يمكن استخدامها بسرعة لتعيين التجارب بشكل منهجي مراجعات.
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which require finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening---the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in a human-machine pipeline for turning low-quality, crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews.
References used
https://aclanthology.org/
rate research
Read More
This paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition. The competition aims at detecting the spans that make a toxic span toxic. In this paper, we demonstrate our system for detecting toxic spans,
which includes expanding the toxic training set with Local Interpretable Model-Agnostic Explanations (LIME), fine-tuning RoBERTa model for detection, and error analysis. We found that feeding the model with an expanded training set using Reddit comments of polarized-toxicity and labeling with LIME on top of logistic regression classification could help RoBERTa more accurately learn to recognize toxic spans. We achieved a span-level F1 score of 0.6715 on the testing phase. Our quantitative and qualitative results show that the predictions from our system could be a good supplement to the gold training set's annotations.
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grain
ed structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.
In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on
popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear. With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings. However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished. We also experiment in cross-genre and cross-lingual settings and find similar trends. We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods.
The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models are known for their high performance in various tasks. However, it is oft
en computationally expensive. A high-end GPU environment is not available in many situations. To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models. Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus. In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.
Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned repre
sentations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for entity linking by 3.0 points of accuracy, and our clustering-based inference model further improves entity linking by 2.3 points.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
