biomedical text mining
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2022 ◽  
pp. 682-693
Author(s):  
Eslam Amer

In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.


Author(s):  
Maliha Rashida ◽  
Fariha Iffath ◽  
Rezaul Karim ◽  
Mohammad Shamsul Arefin

BioChem ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 60-80
Author(s):  
Nícia Rosário-Ferreira ◽  
Catarina Marques-Pereira ◽  
Manuel Pires ◽  
Daniel Ramalhão ◽  
Nádia Pereira ◽  
...  

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.


2021 ◽  
Author(s):  
Mayla R Boguslav ◽  
Nourah M Salem ◽  
Elizabeth K White ◽  
Sonia M Leach ◽  
Lawrence E Hunter

Motivation: Science progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize ignorance statements, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. Results: We present a novel ignorance taxonomy driven by the role ignorance statements play in the research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10,000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. Availability: Corpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python.


2021 ◽  
Author(s):  
Xuan Qin ◽  
Xinzhi Yao ◽  
Jingbo Xia

BACKGROUND Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation. OBJECTIVE The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect. METHODS Four biomedical text mining methods were selected to represent natural language processing methods on drug-related gene mining. Subsequently, a pathway enrichment experiment was performed by using the mined genes, and a series of inverse pathway frequency (IPF) metrics was proposed accordingly to evaluate the effect of pathway enrichment. Thereafter, 7 IPF metrics and traditional <i>P</i> value metrics were compared in simulation experiments to test the robustness of the proposed metrics. RESULTS IPF metrics were evaluated in a case study of rapamycin-related gene set. By applying the best IPF metrics in a pathway enrichment simulation test, a novel discovery of drug efficacy of rapamycin for breast cancer was replicated from the data chosen prior to the year 2000. Our findings show the effectiveness of the best IPF metric in support of knowledge discovery in new drug use. Further, the mechanism underlying the drug-disease association was visualized by Cytoscape. CONCLUSIONS The results of this study suggest the effectiveness of the proposed IPF metrics in pathway enrichment evaluation as well as its application in drug use discovery.


Author(s):  
Mohamed Nadif ◽  
François Role

Abstract Biomedical scientific literature is growing at a very rapid pace, which makes increasingly difficult for human experts to spot the most relevant results hidden in the papers. Automatized information extraction tools based on text mining techniques are therefore needed to assist them in this task. In the last few years, deep neural networks-based techniques have significantly contributed to advance the state-of-the-art in this research area. Although the contribution to this progress made by supervised methods is relatively well-known, this is less so for other kinds of learning, namely unsupervised and self-supervised learning. Unsupervised learning is a kind of learning that does not require the cost of creating labels, which is very useful in the exploratory stages of a biomedical study where agile techniques are needed to rapidly explore many paths. In particular, clustering techniques applied to biomedical text mining allow to gather large sets of documents into more manageable groups. Deep learning techniques have allowed to produce new clustering-friendly representations of the data. On the other hand, self-supervised learning is a kind of supervised learning where the labels do not have to be manually created by humans, but are automatically derived from relations found in the input texts. In combination with innovative network architectures (e.g. transformer-based architectures), self-supervised techniques have allowed to design increasingly effective vector-based word representations (word embeddings). We show in this survey how word representations obtained in this way have proven to successfully interact with common supervised modules (e.g. classification networks) to whose performance they greatly contribute.


Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Yifan Shao ◽  
Haoru Li ◽  
Jinghang Gu ◽  
Longhua Qian ◽  
Guodong Zhou

Abstract Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets


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