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Background: Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. However, due to the complexity of clinical texts, the diversity and large quantity of named entity types, and the unclear boundaries between different entities, existing advanced methods rely to some extent on annotated databases and the scale of embedded dictionaries., Objective: This study aims to address the issues of data scarcity and labeling difficulties in CNER tasks by proposing a dataset augmentation algorithm based on proximity word calculation., Methods: We propose a Segmentation Synonym Sentence Synthesis (SSSS) algorithm based on neighboring vocabulary, which leverages existing public knowledge without the need for manual expansion of specialized domain dictionaries. Through lexical segmentation, the algorithm replaces new synonymous vocabulary by recombining from vast natural language data, achieving nearby expansion expressions of the dataset. We applied the SSSS algorithm to the Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) + conditional random field (CRF) and RoBERTa + Bidirectional Long Short-Term Memory (BiLSTM) + CRF models and evaluated our models (SSSS + RoBERTa + CRF; SSSS + RoBERTa + BiLSTM + CRF) on the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2019 datasets., Results: Our experiments demonstrated that the models SSSS + RoBERTa + CRF and SSSS + RoBERTa + BiLSTM + CRF achieved F1-scores of 91.30% and 91.35% on the CCKS-2017 dataset, respectively. They also achieved F1-scores of 83.21% and 83.01% on the CCKS-2019 dataset, respectively., Conclusions: The experimental results indicated that our proposed method successfully expanded the dataset and remarkably improved the performance of the model, effectively addressing the challenges of data acquisition, annotation difficulties, and insufficient model generalization performance., (© Jian Tang, Zikun Huang, Hongzhen Xu, Hao Zhang, Hailing Huang, Minqiong Tang, Pengsheng Luo, Dong Qin. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/., Competing Interests: None., (© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Mathematical entity recognition is indispensable for machines to accurately explain and depict mathematical content and to enable adequate mathematical operations and reasoning. It expedites automated theorem proving, speeds up the analysis and retrieval of mathematical knowledge from documents, and improves e-learning and educational platforms. It also simplifies translation, scientific research, data analysis, interpretation, and the practical application of mathematical information. Mathematical entity recognition in the Bangla language is novel; to our best knowledge, no other similar works have been done. Here, we identify the mathematical operator, operands as numbers, and popular mathematical terms (complex numbers, real numbers, prime numbers, etc.). In this work, we recognize Bangla Mathematical Entity Recognition (MER) utilizing the ensemble architecture of deep neural networks known as Bidirectional Encoder Representations from Transformers (BERT). We prepare a novel dataset comprising 13,717 observations, each containing a mathematical statement, mathematical entity, and mathematical type. In our recognition process, we consider our proposed architectures using accuracy, precision, recall and f1-score as the performance metrics. The results have shown a satisfactory accuracy percentage of 97.98 with BERT and 99.76% with ensemble BERT., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Objectives: To investigate the feasibility and performance of Chat Generative Pretrained Transformer (ChatGPT) in converting symptom narratives into structured symptom labels., Methods: We extracted symptoms from 300 deidentified symptom narratives of COVID-19 patients by a computer-based matching algorithm (the standard), and prompt engineering in ChatGPT. Common symptoms were those with a prevalence >10% according to the standard, and similarly less common symptoms were those with a prevalence of 2-10%. The precision of ChatGPT was compared with the standard using sensitivity and specificity with 95% exact binomial CIs (95% binCIs). In ChatGPT, we prompted without examples (zero-shot prompting) and with examples (few-shot prompting)., Results: In zero-shot prompting, GPT-4 achieved high specificity (0.947 [95% binCI: 0.894-0.978]-1.000 [95% binCI: 0.965-0.988, 1.000]) for all symptoms, high sensitivity for common symptoms (0.853 [95% binCI: 0.689-0.950]-1.000 [95% binCI: 0.951-1.000]), and moderate sensitivity for less common symptoms (0.200 [95% binCI: 0.043-0.481]-1.000 [95% binCI: 0.590-0.815, 1.000]). Few-shot prompting increased the sensitivity and specificity. GPT-4 outperformed GPT-3.5 in response accuracy and consistent labelling., Discussion: This work substantiates ChatGPT's role as a research tool in medical fields. Its performance in converting symptom narratives to structured symptom labels was encouraging, saving time and effort in compiling the task-specific training data. It potentially accelerates free-text data compilation and synthesis in future disease outbreaks and improves the accuracy of symptom checkers. Focused prompt training addressing ambiguous descriptions impacts medical research positively., (Copyright © 2023 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Objective: Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types., Methods: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses., Results: We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy., Significance: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subsequently the development of precision medicine for epilepsy patients., (© 2023 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

The upsurge of multifarious endeavors across scientific fields propelled Big Data in the scientific domain. Despite the advancements in management systems, researchers find that mathematical knowledge remains one of the most challenging to manage due to the latter's inherent heterogeneity. One novel recourse being explored is variable typing where current works remain preliminary and, thus, provide a wide room for contribution. In this study, a primordial attempt to implement the end-to-end Entity Recognition (ER) and Relation Extraction (RE) approach to variable typing was made using the BERT (Bidirectional Encoder Representations from Transformers) model. A micro-dataset was developed for this process. According to our findings, the ER model and RE model, respectively, have Precision of 0.8142 and 0.4919, Recall of 0.7816 and 0.6030, and F1-Scores of 0.7975 and 0.5418. Despite the limited dataset, the models performed at par with values in the literature. This work also discusses the factors affecting this BERT-based approach, giving rise to suggestions for future implementations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Introduction: In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain., Methods: To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest., Results and Discussion: Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Lu, Jiang, Shi, Zhong, Gu, Huangfu and Gong.)

Background: Knowledge graphs are a powerful tool for organizing knowledge, processing information and integrating scattered information, effectively visualizing the relationships among entities and supporting further intelligent applications. One of the critical tasks in building knowledge graphs is knowledge extraction. The existing knowledge extraction models in the Chinese medical domain usually require high-quality and large-scale manually labeled corpora for model training. In this study, we investigate rheumatoid arthritis (RA)-related Chinese electronic medical records (CEMRs) and address the automatic knowledge extraction task with a small number of annotated samples from CEMRs, from which an authoritative RA knowledge graph is constructed., Methods: After constructing the domain ontology of RA and completing manual labeling, we propose the MC-bidirectional encoder representation from transformers-bidirectional long short-term memory-conditional random field (BERT-BiLSTM-CRF) model for the named entity recognition (NER) task and the MC-BERT + feedforward neural network (FFNN) model for the entity extraction task. The pretrained language model (MC-BERT) is trained with many unlabeled medical data and fine-tuned using other medical domain datasets. We apply the established model to automatically label the remaining CEMRs, and then an RA knowledge graph is constructed based on the entities and entity relations, a preliminary assessment is conducted, and an intelligent application is presented., Results: The proposed model achieved better performance than that of other widely used models in knowledge extraction tasks, with mean F1 scores of 92.96% in entity recognition and 95.29% in relation extraction. This study preliminarily confirmed that using a pretrained medical language model could solve the problem that knowledge extraction from CEMRs requires a large number of manual annotations. An RA knowledge graph based on the above identified entities and extracted relations from 1,986 CEMRs was constructed. Experts verified the effectiveness of the constructed RA knowledge graph., Conclusions: In this paper, an RA knowledge graph based on CEMRs was established, the processes of data annotation, automatic knowledge extraction, and knowledge graph construction were described, and a preliminary assessment and an application were presented. The study demonstrated the viability of a pretrained language model combined with a deep neural network for knowledge extraction tasks from CEMRs based on a small number of manually annotated samples., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1158/coif). ML is an employee of Beijing Lanzhou Technology Co., Ltd. The other authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology. Knowledge graphs (KGs) have now been widely used in both industry and academia to address real-world problems. Despite the abundance of generic KGs, there is a vital need to build domain-specific knowledge graphs in the marine sciences domain. In addition, there is still not an effective method for named entity recognition when constructing a knowledge graph, especially when including data from both scientific and social media sources. This article presents a novel marine science domain-based knowledge graph framework. This framework involves capturing marine domain data into KG representations. The proposed approach utilizes various entity information based on marine domain experts to enrich the semantic content of the knowledge graph. To enhance named entity recognition accuracy, we propose a novel TrellisNet-CRF model. Our experiment results demonstrate that the TrellisNet-CRF model reached a 96.99% accuracy rate for marine domain named entity recognition, which outperforms the current state-of-the-art baseline. The effectiveness of the TrellisNet-CRF module was then further demonstrated and confirmed on entity recognition and visualization tasks., Competing Interests: The authors declare that they have no competing interests., (© 2022 Wu et al.)

The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder-decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Han, Wang and Wang.)

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n -grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset ( p < 0.01). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.

Background: Extracting entities and their relationships from electronic medical records (EMRs) is an important research direction in the development of medical informatization. Recently, a method was proposed to transform entity relation extraction into entity recognition by using annotation rules, and then solve the problem of relation extraction by an entity recognition model. However, this method cannot deal with one-to-many entity relationship problems., Methods: This paper combined the bidirectional long- and short-term memory-conditional random field (BiLSTM-CRF) deep learning model with an improvement of sequence annotation rules, hided relationships between entities in entity labels, then the problem of one-to-many named entity relation extraction in EMRs was transformed into entity recognition based on relation sets, and entity extraction was carried out through the entity recognition model., Results: Entity extraction was achieved through the entity recognition model. The result of entity recognition was transformed into the corresponding entity relationship, thus completing the task of one-to-many entity relation extraction by the improved annotation rules, the accuracy rate of proposed method reaches 83.46%, the recall rate is 81.12%, and the value of comprehensive index F1 is 0.8227., Conclusions: Through the annotation analysis of EMRs, our experimental results show that the improved annotation rules can effectively complete the task of one-to-many medical entity relation extraction from EMRs., Competing Interests: Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-3828). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Corpora are one of the most valuable resources at present for building machine learning systems. However, building new corpora is an expensive task, which makes the automatic extension of corpora a highly attractive task to develop. Hence, finding new strategies that reduce the cost and effort involved in this task, while at the same time guaranteeing quality, remains an open and important challenge for the research community. In this paper, we present a set of ensembling strategies oriented toward entity and relation extraction tasks. The main goal is to combine several automatically annotated versions of corpora to produce a single version with improved quality. An ensembler is built by exploring a configuration space in search of the combination that maximizes the fitness of the ensembled collection according to a reference collection. The eHealth-KD 2019 challenge was chosen for the case study. The submitted systems' outputs were ensembled, resulting in the construction of an automatically annotated collection of 8000 sentences. We show that using this collection as additional training input for a baseline algorithm has a positive impact on its performance. Additionally, the ensembling pipeline was used as a participant system in the 2020 edition of the challenge. The ensembled run achieved a slightly better performance than the individual runs., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet., Objective: The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil., Methods: This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts., Results: In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic., Conclusions: This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities., (©Tiago de Melo, Carlos M S Figueiredo. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 10.02.2021.)

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. We performed experiments on body classification task, and the F1 values reached 90.65%. We also performed experiments on anatomic region recognition task, and the F1 values reached 93.89%. On both tasks, our model had higher performance than state-of-the-art models, such as Bi-LSTM-CRF, Bi-LSTM-Attention, and Vote. Through experiments, our model has a good effect when dealing with small frequency entities and unknown entities; with a small training dataset, our method showed 2-4% improvement on F1 value compared to the basic Bi-LSTM-CRF models. Additionally, on anatomic region recognition task, besides using our proposed entity extraction model, 12 rules we designed and domain dictionary were adopted. Then, in this task, the weighted F1 value of the three specific entities extraction reached 84.36%.

In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database to construct the knowledge graph of knee osteoarthritis. The experiment proves that the knowledge graph is comprehensive and reliable, and the knowledge graph construction method proposed in this study is effective., (© Springer Nature Switzerland AG 2020.)

Background: Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers., Results: To remedy the above issues, we present a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). On one hand, a large amount of entity name knowledge extracted from biomedical knowledge bases is used to recognize more entity variants. On the other hand, structural knowledge of entities is extracted and encoded as identifier (ID) embeddings, which are then used for better entity normalization. Moreover, deep contextualized word representations generated by pre-trained language models are also incorporated into our knowledge-enhanced system for modeling multi-sense information of entities. Experimental results on the BioCreative VI Bio-ID corpus show that our proposed knowledge-enhanced system achieves 0.871 F1-score for PNER and 0.445 F1-score for PNEN, respectively, leading to a new state-of-the-art performance., Conclusions: We propose a knowledge-enhanced system that combines both entity knowledge and deep contextualized word representations. Comparison results show that entity knowledge is beneficial to the PNER and PNEN task and can be well combined with contextualized information in our system for further improvement.

Background: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels., Results: In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively., Conclusion: Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.

Objectives: Treatment used in a randomized clinical trial is a critical data element both for physicians at the point of care and reviewers who are evaluating different interventions. Much of existing work on treatment extraction from the biomedical literature has focused on the extraction of pharmacological interventions. However, non-pharmacological interventions (e.g., exercise, diet, etc.) that are frequently used to address chronic conditions are less well studied. The goal of this study is to compare knowledge-based and machine learning strategies for the extraction of both drug and non-drug treatments., Methods: We collected 800 randomized clinical trial abstracts each for breast cancer and diabetes from PubMed. The treatments in the result/conclusion sentences of the abstracts were manually annotated and marked as drug/non-drug treatments. We then designed three methods to identify the treatments and evaluated the systems with respect to drug/non-drug treatments. The first method is solely based on knowledge base (here we used MetaMap). The second method is based on a machine learning model trained mainly on contextual features (ML&#95;only). The third method is a combination approach that integrates the previous two approaches., Results/discussion: Results show that MetaMap, when used with high precision semantic types, has better performance for drug compared to non-drug treatments (F1 = 0.77 vs. 0.64). The ML&#95;only approach has smaller performance difference between drug and non-drug treatments compared with the KB-based approach (F1 = 0.02 vs. 0.05, 0.07, and 0.13). The combination approach achieves significantly better performance than all MetaMap approaches alone for total treatments (F1 = 0.76 vs. 0.72, p < 0.001). The performance gain mainly comes from the non-drug treatments (0.03-0.08 improvement in F1), while the drug treatments do not benefit much from the combination approach (0-0.03 improvement in F1)., Conclusion: These results suggest that a knowledge-based approach should be employed for medical conditions that are primarily treated with drugs whereas conditions that are treated with either a combination of drug and non-drug interventions or primarily non-drug interventions should use automated tools that combine machine learning and a knowledge-based approach to achieve optimal performance., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background and Objective: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and to prepare the patient to manage the future difficulties. Rare disease associations are making an effort to manually collect this information, but it is a long process. A lot of information about the consequences of rare diseases is published in scientific papers, and our goal is to automatically extract disabilities associated with diseases from them., Methods: This work presents a new corpus of abstracts from scientific papers related to rare diseases, which has been manually annotated with disabilities. This corpus allows to train machine and deep learning systems that can automatically process other papers, thus extracting new information about the relations between rare diseases and disabilities. The corpus is also annotated with negation and speculation when they appear affecting disabilities. The corpus has been made publicly accessible., Results: We have devised some experiments using deep learning techniques to show the usefulness of the developed corpus. Specifically, we have designed a long short-term memory based architecture for disabilities identification, as well as a convolutional neural network for detecting their relationships to diseases. The systems designed do not need any preprocessing of the data, but only low dimensional vectors representing the words., Conclusions: The developed corpus will allow to train systems to identify disabilities in biomedical documents, which the current annotation systems are not able to detect. The system could also be trained to detect relationships between them and diseases, as well as negation and speculation, that can change the meaning of the language. The deep learning models designed for identifying disabilities and their relationships to diseases in new documents show that the corpus allows obtaining an F-measure of around 81% for the disability recognition and 75% for relation extraction., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Full-text scientific articles are increasingly available, but capturing the meaning conveyed within an article automatically remains a bottleneck for semantic search and reasoning systems. In this paper we consider elliptical coordinated compound noun phrases that authors use to save space in an article. Systems that do not attend to coordination would incorrectly interpret "breast or lung cancer" as a body part (breast) and a disease (lung cancer) rather than two diseases. The algorithmic approach introduced in this paper uses a generate-and-test strategy where candidate expansions for forward, backward and complex ellipses are generated from syntactic dependencies. Dependencies are also used to create a dictionary of non-coordinated noun phrases that is used during the test phrase. Experiments on 21,280 full-text articles show that more than a million noun phrases were impacted by coordinated ellipses. The system achieves 73.07% precision, 75.38% recall, 74.23% F-score and 94.72% accuracy for new noun phrases in the development set. The precision was higher for backward (82.62 vs. 78.63) and forward expansions (64.82 vs. 60.17) and lower for complex expansions (63.41 vs. 72.59) in a test set. On average 10.79% of all noun phrases would be missed if coordination were not resolved, which corresponds to 48 new noun phrases per article in the journal Carcinogenesis, 52 new phrases per article in Diabetes, and 56 new phrases per article in Endocrinology. Results also show coordinated ellipses are more prevalent in abstracts (12.31% of all noun phrases) than in the body of an article (10.70%). To further test the generalizability of this approach the system (without modification) was used on two new collections., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts., Methods: In this paper, we comprehensively investigate the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition. The LSTM model consists of three layers: input layer - generates representation of each word of a sentence; LSTM layer - outputs another word representation sequence that captures the context information of each word in this sentence; Inference layer - makes tagging decisions according to the output of LSTM layer, that is, outputting a label sequence., Results: Experiments conducted on corpora of the 2010, 2012 and 2014 i2b2 NLP challenges show that LSTM achieves highest micro-average F1-scores of 85.81% on the 2010 i2b2 medical concept extraction, 92.29% on the 2012 i2b2 clinical event detection, and 94.37% on the 2014 i2b2 de-identification, which is considerably competitive with other state-of-the-art systems., Conclusions: LSTM that requires no hand-crafted feature has great potential on entity recognition from clinical texts. It outperforms traditional machine learning methods that suffer from fussy feature engineering. A possible future direction is how to integrate knowledge bases widely existing in the clinical domain into LSTM, which is a case of our future work. Moreover, how to use LSTM to recognize entities in specific formats is also another possible future direction.

Background: Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above., Results: Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction., Conclusions: The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.