An active learning-enabled annotation system for clinical named entity recognition

Abstract Background Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain. Methods In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models. Results The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users. Conclusions We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.https://deepblue.lib.umich.edu/bitstream/2027.42/137676/1/12911_2017_Article_466.pd

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

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

LeafAI: query generator for clinical cohort discovery rivaling a human programmer

Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. Materials and Methods: The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible across 8 clinical trials, compared to 27% matched and 14,587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival a human programmer in finding patients eligible for clinical trials

The devices, experimental scaffolds, and biomaterials ontology (DEB): a tool for mapping, annotation, and analysis of biomaterials' data

The size and complexity of the biomaterials literature makes systematic data analysis an excruciating manual task. A practical solution is creating databases and information resources. Implant design and biomaterials research can greatly benefit from an open database for systematic data retrieval. Ontologies are pivotal to knowledge base creation, serving to represent and organize domain knowledge. To name but two examples, GO, the gene ontology, and CheBI, Chemical Entities of Biological Interest ontology and their associated databases are central resources to their respective research communities. The creation of the devices, experimental scaffolds, and biomaterials ontology (DEB), an open resource for organizing information about biomaterials, their design, manufacture, and biological testing, is described. It is developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus, systematically curated to represent the domain's lexicon. Topics covered are validated by members of the biomaterials research community. The ontology may be used for searching terms, performing annotations for machine learning applications, standardized meta-data indexing, and other cross-disciplinary data exploitation. The input of the biomaterials community to this effort to create data-driven open-access research tools is encouraged and welcomed.Preprin

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed