Terminology Extraction for and from Communications in Multi-disciplinary Domains

Terminology extraction generally refers to methods and systems for identifying term candidates in a uni-disciplinary and uni-lingual environment such as engineering, medical, physical and geological sciences, or administration, business and leisure. However, as human enterprises get more and more complex, it has become increasingly important for teams in one discipline to collaborate with others from not only a non-cognate discipline but also speaking a different language. Disaster mitigation and recovery, and conflict resolution are amongst the areas where there is a requirement to use standardised multilingual terminology for communication. This paper presents a feasibility study conducted to build terminology (and ontology) in the domain of disaster management and is part of the broader work conducted for the EU project Sland \ub4 ail (FP7 607691). We have evaluated CiCui (for Chinese name \ub4 \u8bcd\u8403, which translates to words gathered), a corpus-based text analytic system that combine frequency, collocation and linguistic analyses to extract candidates terminologies from corpora comprised of domain texts from diverse sources. CiCui was assessed against four terminology extraction systems and the initial results show that it has an above average precision in extracting terms

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.Peer ReviewedPostprint (published version

BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

From narrative descriptions to MedDRA: automagically encoding adverse drug reactions

The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs). In such task qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day. Natural Language Processing (NLP) applications can support the work of people responsible for pharmacovigilance. Our objective is to develop NLP algorithms and tools for the detection of ADR clinical terminology. Efficient applications can concretely improve the quality of the experts\u2019 revisions. NLP software can quickly analyze narrative texts and offer an encoding (i.e., a list of MedDRA terms) that the expert has to revise and validate. MagiCoder, an NLP algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure is efficient in terms of computational complexity. We tested MagiCoder through several experiments. In the first one, we tested it on a large dataset of about 4500 manually revised reports, by performing an automated comparison between human and MagiCoder encoding. Moreover, we tested MagiCoder on a set of about 1800 reports, manually revised ex novo by some experts of the domain, who also compared automatic solutions with the gold reference standard. We also provide two initial experiments with reports written in English, giving a first evidence of the robustness of MagiCoder w.r.t. the change of the language. For the current base version of MagiCoder, we measured an average recall and precision of and , respectively. From a practical point of view, MagiCoder reduces the time required for encoding ADR reports. Pharmacologists have only to review and validate the MedDRA terms proposed by the application, instead of choosing the right terms among the 70\u202fK low level terms of MedDRA. Such improvement in the efficiency of pharmacologists\u2019 work has a relevant impact also on the quality of the subsequent data analysis. We developed MagiCoder for the Italian pharmacovigilance language. However, our proposal is based on a general approach, not depending on the considered language nor the term dictionary

Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

약물 감시를 위한 비정형 텍스트 내 임상 정보 추출 연구

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 응용바이오공학과, 2023. 2. 이형기.Pharmacovigilance is a scientific activity to detect, evaluate and understand the occurrence of adverse drug events or other problems related to drug safety. However, concerns have been raised over the quality of drug safety information for pharmacovigilance, and there is also a need to secure a new data source to acquire drug safety information. On the other hand, the rise of pre-trained language models based on a transformer architecture has accelerated the application of natural language processing (NLP) techniques in diverse domains. In this context, I tried to define two problems in pharmacovigilance as an NLP task and provide baseline models for the defined tasks: 1) extracting comprehensive drug safety information from adverse drug events narratives reported through a spontaneous reporting system (SRS) and 2) extracting drug-food interaction information from abstracts of biomedical articles. I developed annotation guidelines and performed manual annotation, demonstrating that strong NLP models can be trained to extracted clinical information from unstructrued free-texts by fine-tuning transformer-based language models on a high-quality annotated corpus. Finally, I discuss issues to consider when when developing annotation guidelines for extracting clinical information related to pharmacovigilance. The annotated corpora and the NLP models in this dissertation can streamline pharmacovigilance activities by enhancing the data quality of reported drug safety information and expanding the data sources.약물 감시는 약물 부작용 또는 약물 안전성과 관련된 문제의 발생을 감지, 평가 및 이해하기 위한 과학적 활동이다. 그러나 약물 감시에 사용되는 의약품 안전성 정보의 보고 품질에 대한 우려가 꾸준히 제기되었으며, 해당 보고 품질을 높이기 위해서는 안전성 정보를 확보할 새로운 자료원이 필요하다. 한편 트랜스포머 아키텍처를 기반으로 사전훈련 언어모델이 등장하면서 다양한 도메인에서 자연어처리 기술 적용이 가속화되었다. 이러한 맥락에서 본 학위 논문에서는 약물 감시를 위한 다음 2가지 정보 추출 문제를 자연어처리 문제 형태로 정의하고 관련 기준 모델을 개발하였다: 1) 수동적 약물 감시 체계에 보고된 이상사례 서술자료에서 포괄적인 약물 안전성 정보를 추출한다. 2) 영문 의약학 논문 초록에서 약물-식품 상호작용 정보를 추출한다. 이를 위해 안전성 정보 추출을 위한 어노테이션 가이드라인을 개발하고 수작업으로 어노테이션을 수행하였다. 결과적으로 고품질의 자연어 학습데이터를 기반으로 사전학습 언어모델을 미세 조정함으로써 비정형 텍스트에서 임상 정보를 추출하는 강력한 자연어처리 모델 개발이 가능함을 확인하였다. 마지막으로 본 학위 논문에서는 약물감시와 관련된임상 정보 추출을 위한 어노테이션 가이드라인을 개발할 때 고려해야 할 주의 사항에 대해 논의하였다. 본 학위 논문에서 소개한 자연어 학습데이터와 자연어처리 모델은 약물 안전성 정보의 보고 품질을 향상시키고 자료원을 확장하여 약물 감시 활동을 보조할 것으로 기대된다.Chapter 1 1 1.1 Contributions of this dissertation 2 1.2 Overview of this dissertation 2 1.3 Other works 3 Chapter 2 4 2.1 Pharmacovigilance 4 2.2 Biomedical NLP for pharmacovigilance 6 2.2.1 Pre-trained language models 6 2.2.2 Corpora to extract clinical information for pharmacovigilance 9 Chapter 3 11 3.1 Motivation 12 3.2 Proposed Methods 14 3.2.1 Data source and text corpus 15 3.2.2 Annotation of ADE narratives 16 3.2.3 Quality control of annotation 17 3.2.4 Pretraining KAERS-BERT 18 3.2.6 Named entity recognition 20 3.2.7 Entity label classification and sentence extraction 21 3.2.8 Relation extraction 21 3.2.9 Model evaluation 22 3.2.10 Ablation experiment 23 3.3 Results 24 3.3.1 Annotated ICSRs 24 3.3.2 Corpus statistics 26 3.3.3 Performance of NLP models to extract drug safety information 28 3.3.4 Ablation experiment 31 3.4 Discussion 33 3.5 Conclusion 38 Chapter 4 39 4.1 Motivation 39 4.2 Proposed Methods 43 4.2.1 Data source 44 4.2.2 Annotation 45 4.2.3 Quality control of annotation 49 4.2.4 Baseline model development 49 4.3 Results 50 4.3.1 Corpus statistics 50 4.3.2 Annotation Quality 54 4.3.3 Performance of baseline models 55 4.3.4 Qualitative error analysis 56 4.4 Discussion 59 4.5 Conclusion 63 Chapter 5 64 5.1 Issues around defining a word entity 64 5.2 Issues around defining a relation between word entities 66 5.3 Issues around defining entity labels 68 5.4 Issues around selecting and preprocessing annotated documents 68 Chapter 6 71 6.1 Dissertation summary 71 6.2 Limitation and future works 72 6.2.1 Development of end-to-end information extraction models from free-texts to database based on existing structured information 72 6.2.2 Application of in-context learning framework in clinical information extraction 74 Chapter 7 76 7.1 Annotation Guideline for "Extraction of Comprehensive Drug Safety Information from Adverse Event Narratives Reported through Spontaneous Reporting System" 76 7.2 Annotation Guideline for "Extraction of Drug-Food Interactions from the Abtracts of Biomedical Articles" 100박

DrugExBERT for Pharmacovigilance – A Novel Approach for Detecting Drug Experiences from User-Generated Content

Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data- and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness