Purpose

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

Design/methodology/approach

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

Findings

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

Research limitations/implications

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

Practical implications

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

Social implications

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

Originality/value

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

中文翻译：

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ABEE：从生物医学文本文档中自动提取生物实体

目的

这项研究的目的是设计一个多任务学习模型，以便可以从生物医学文本中提取生物医学实体而不会产生任何歧义。

设计/方法/途径

在所提出的自动生物实体提取 (ABEE) 模型中，结合单任务学习模型引入了多任务学习模型。我们的模型使用来自 Transformers 的双向编码器表示来训练单任务学习模型。然后组合模型的输出，以便我们可以从生物医学文本中找到实体的真实性。

发现

拟议的 ABEE 模型针对生物医学文本中的独特基因/蛋白质、化学和疾病实体。就药物发现和临床试验等生物医学研究而言，这一发现更为重要。这项研究不仅有助于减少研究人员的工作量，而且有助于降低新药发现和新疗法的成本。

研究局限性/影响

因此，该模型没有任何限制，但研究团队计划用千兆字节的数据测试该模型并建立知识图谱，以便研究人员可以轻松估计相似群体的实体。

实际影响

就实际意义而言，ABEE模型将有助于信息抽取（IE）等各种自然语言处理任务，在生物医学命名实体识别和生物医学关系抽取以及信息检索任务中发挥重要作用比如基于文献的知识发现。

社会影响

在 COVID-19 大流行期间，由于当时临床试验的增加，对我们这类工作的需求增加了。如果之前引入了此类研究，那么它将减少该领域新药发现的时间和精力。

原创性/价值

在这项工作中，我们提出了一种新颖的多任务学习模型，该模型能够从生物医学文本中提取生物医学实体而不会产生任何歧义。所提出的模型在精度、召回率和 F1 分数方面实现了最先进的性能。