AuDis: an automatic CRF-enhanced disease normalization in biomedical text

A combined recall and rank framework with online negative sampling for chinese procedure terminology normalization

MOTIVATION

Medical terminology normalization aims to map the clinical mention to terminologies coming from a knowledge base, which plays an important role in analyzing Electronic Health Record (EHR) and many downstream tasks. In this paper, we focus on Chinese procedure terminology normalization. The expressions of terminology are various and one medical mention may be linked to multiple terminologies. Existing studies based on Learning To Rank (LTR) does not fully consider the quality of negative samples during model training and the importance of keywords in this domain-specific task.

RESULTS

We propose a combined recall and rank framework to solve these problems. A pair-wise Bert model with deep metric learning is used to recall candidates. Previous methods either train Bert in a point-wise way or based on a multi-class classification problem, which may lead serious efficiency problems or not be effective enough. During model training, we design a novel online negative sampling algorithm to activate the pair-wise method. To deal with multi-implication scenarios, we train the task of implication number prediction together with the recall task in a multi-task learning setting, since these two tasks are highly complementary. In rank step, we propose a keywords attentive mechanism to focus on domain-specific information such as procedure sites and procedure types. Finally, a fusion block merges the results of the recall and the rank model. Detailed experimental analysis shows our proposed framework has a remarkable improvement on both performance and efficiency.

AVAILABILITY

The source code will be available at https://github.com/sxthunder/CMTN upon publication.

Recent advances in biomedical literature mining

The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.

The Russian Drug Reaction Corpus and neural models for drug reactions and effectiveness detection in user reviews

MOTIVATION

Drugs and diseases play a central role in many areas of biomedical research and healthcare. Aggregating knowledge about these entities across a broader range of domains and languages is critical for information extraction (IE) applications. To facilitate text mining methods for analysis and comparison of patient's health conditions and adverse drug reactions reported on the Internet with traditional sources such as drug labels, we present a new corpus of Russian language health reviews.

RESULTS

The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharmaceutical products. The corpus itself consists of two parts, the raw one and the labeled one. The raw part includes 1.4 million health-related user-generated texts collected from various Internet sources, including social media. The labeled part contains 500 consumer reviews about drug therapy with drug- and disease-related information. Labels for sentences include health-related issues or their absence. The sentences with one are additionally labeled at the expression level for identification of fine-grained subtypes such as drug classes and drug forms, drug indications and drug reactions. Further, we present a baseline model for named entity recognition (NER) and multilabel sentence classification tasks on this corpus. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. For the sentence classification task, our model achieves the macro F1 score of 68.82% gaining 7.47% over the score of BERT model trained on Russian data.

AVAILABILITY AND IMPLEMENTATION

We make the RuDReC corpus and pretrained weights of domain-specific BERT models freely available at https://github.com/cimm-kzn/RuDReC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model

With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .

Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)-based ranking for concept normalization

OBJECTIVE

Concept normalization, the task of linking phrases in text to concepts in an ontology, is useful for many downstream tasks including relation extraction, information retrieval, etc. We present a generate-and-rank concept normalization system based on our participation in the 2019 National NLP Clinical Challenges Shared Task Track 3 Concept Normalization.

MATERIALS AND METHODS

The shared task provided 13 609 concept mentions drawn from 100 discharge summaries. We first design a sieve-based system that uses Lucene indices over the training data, Unified Medical Language System (UMLS) preferred terms, and UMLS synonyms to generate a list of possible concepts for each mention. We then design a listwise classifier based on the BERT (Bidirectional Encoder Representations from Transformers) neural network to rank the candidate concepts, integrating UMLS semantic types through a regularizer.

RESULTS

Our generate-and-rank system was third of 33 in the competition, outperforming the candidate generator alone (81.66% vs 79.44%) and the previous state of the art (76.35%). During postevaluation, the model's accuracy was increased to 83.56% via improvements to how training data are generated from UMLS and incorporation of our UMLS semantic type regularizer.

DISCUSSION

Analysis of the model shows that prioritizing UMLS preferred terms yields better performance, that the UMLS semantic type regularizer results in qualitatively better concept predictions, and that the model performs well even on concepts not seen during training.

CONCLUSIONS

Our generate-and-rank framework for UMLS concept normalization integrates key UMLS features like preferred terms and semantic types with a neural network-based ranking model to accurately link phrases in text to UMLS concepts.

Annotating and detecting phenotypic information for chronic obstructive pulmonary disease

Objectives

Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information.

Materials and methods

Since COPD phenotype descriptions often mention other concepts within them (proteins, treatments, etc.), our corpus annotations include both outermost phenotype descriptions and concepts nested within them. Our neural layered bidirectional long short-term memory conditional random field (BiLSTM-CRF) network firstly recognizes nested mentions, which are fed into subsequent BiLSTM-CRF layers, to help to recognize enclosing phenotype mentions.

Results

Our corpus of 30 full papers (available at: http://www.nactem.ac.uk/COPD) is annotated by experts with 27 030 phenotype-related concept mentions, most of which are automatically linked to UMLS Metathesaurus concepts. When trained using the corpus, our BiLSTM-CRF network outperforms other popular approaches in recognizing detailed phenotypic information.

Discussion

Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, for example, those specifically concerning reactions to treatments.

Conclusion

The importance of our corpus for developing methods to extract fine-grained information about COPD phenotypes is demonstrated through its successful use to train a layered BiLSTM-CRF network to extract phenotypic information at various levels of granularity. The minimal human intervention needed for training should permit ready adaption to extracting phenotypic information about other diseases.

Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition

BACKGROUND

Disease named entity recognition (NER) plays an important role in biomedical research. There are a significant number of challenging issues to be addressed; among these, the identification of rare diseases and complex disease names and the problem of tagging inconsistency (i.e., if an entity is tagged differently in a document) are attracting substantial research attention.

METHODS

We propose a new neural network method named Dic-Att-BiLSTM-CRF (DABLC) for disease NER. DABLC applies an efficient exact string matching method to match disease entities with a disease dictionary; here, the dictionary is constructed based on the Disease Ontology. Furthermore, DABLC constructs a dictionary attention layer by incorporating a disease dictionary matching method and document-level attention mechanism. Finally, a bidirectional long short-term memory network and conditional random field (BiLSTM-CRF) with a dictionary attention layer is proposed to combine the disease dictionary to develop disease NER.

RESULTS

Extensive experiments are conducted on two widely-used corpora: the NCBI disease corpus and the BioCreative V CDR corpus. We apply each test on 10 executions of each model, with a 95% confidence interval. DABLC achieves the highest F1 scores (NCBI: Precision = 0.883, Recall = 0.89, F1 = 0.886; BioCreative V CDR: Precision = 0.891, Recall = 0.875, F1 = 0.883), outperforming the state-of-the-art methods.

CONCLUSION

DABLC combines the advantages of both external dictionary resources and deep attention neural networks. This aids the identification of rare diseases and complex disease names; moreover, it reduces the impact of tagging inconsistency. Special disease NER and deep learning models addressing long sentences are noteworthy areas for future examination.

Annotation and detection of drug effects in text for pharmacovigilance

Pharmacovigilance (PV) databases record the benefits and risks of different drugs, as a means to ensure their safe and effective use. Creating and maintaining such resources can be complex, since a particular medication may have divergent effects in different individuals, due to specific patient characteristics and/or interactions with other drugs being administered. Textual information from various sources can provide important evidence to curators of PV databases about the usage and effects of drug targets in different medical subjects. However, the efficient identification of relevant evidence can be challenging, due to the increasing volume of textual data. Text mining (TM) techniques can support curators by automatically detecting complex information, such as interactions between drugs, diseases and adverse effects. This semantic information supports the quick identification of documents containing information of interest (e.g., the different types of patients in which a given adverse drug reaction has been observed to occur). TM tools are typically adapted to different domains by applying machine learning methods to corpora that are manually labelled by domain experts using annotation guidelines to ensure consistency. We present a semantically annotated corpus of 597 MEDLINE abstracts, PHAEDRA, encoding rich information on drug effects and their interactions, whose quality is assured through the use of detailed annotation guidelines and the demonstration of high levels of inter-annotator agreement (e.g., 92.6% F-Score for identifying named entities and 78.4% F-Score for identifying complex events, when relaxed matching criteria are applied). To our knowledge, the corpus is unique in the domain of PV, according to the level of detail of its annotations. To illustrate the utility of the corpus, we have trained TM tools based on its rich labels to recognise drug effects in text automatically. The corpus and annotation guidelines are available at: http://www.nactem.ac.uk/PHAEDRA/ .

Information Retrieval and Text Mining Technologies for Chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

The Comparative Toxicogenomics Database: update 2017

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.