Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles

Precise geocoding and time normalization for text requires that location and time phrases be identified. Many state-of-the-art geoparsers and temporal parsers suffer from low recall. Categories commonly missed by parsers are: nouns used in a non- spatiotemporal sense, adjectival and adverbial phrases, prepositional phrases, and numerical phrases. We collected and annotated data set by querying commercial web searches API with such spatiotemporal expressions as were missed by state-of-the- art parsers. Due to the high cost of sentence annotation, active learning was used to label training data, and a new strategy was designed to better select training examples to reduce labeling cost. For the learning algorithm, we applied an average perceptron trained Featurized Hidden Markov Model (FHMM). Five FHMM instances were used to create an ensemble, with the output phrase selected by voting. Our ensemble model was tested on a range of sequential labeling tasks, and has shown competitive performance. Our contributions include (1) an new dataset annotated with named entities and expanded spatiotemporal expressions; (2) a comparison of inference algorithms for ensemble models showing the superior accuracy of Belief Propagation over Viterbi Decoding; (3) a new example re-weighting method for active ensemble learning that 'memorizes' the latest examples trained; (4) a spatiotemporal parser that jointly recognizes expanded spatiotemporal expressions as well as named entities.Comment: 10 page

HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

<p>Abstract</p> <p>Background</p> <p>The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.</p> <p>Results</p> <p>We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.</p> <p>Conclusion</p> <p>Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.</p

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves

Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

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

Evaluation of a Concept Mapping Task Using Named Entity Recognition and Normalization in Unstructured Clinical Text.

In this pilot study, we explore the feasibility and accuracy of using a query in a commercial natural language processing engine in a named entity recognition and normalization task to extract a wide spectrum of clinical concepts from free text clinical letters. Editorial guidance developed by two independent clinicians was used to annotate sixty anonymized clinic letters to create the gold standard. Concepts were categorized by semantic type, and labels were applied to indicate contextual attributes such as negation. The natural language processing (NLP) engine was Linguamatics I2E version 5.3.1, equipped with an algorithm for contextualizing words and phrases and an ontology of terms from Intelligent Medical Objects to which those tokens were mapped. Performance of the engine was assessed on a training set of the documents using precision, recall, and the F1 score, with subset analysis for semantic type, accurate negation, exact versus partial conceptual matching, and discontinuous text. The engine underwent tuning, and the final performance was determined for a test set. The test set showed an F1 score of 0.81 and 0.84 using strict and relaxed criteria respectively when appropriate negation was not required and 0.75 and 0.77 when it was. F1 scores were higher when concepts were derived from continuous text only. This pilot study showed that a commercially available NLP engine delivered good overall results for identifying a wide spectrum of structured clinical concepts. Such a system holds promise for extracting concepts from free text to populate problem lists or for data mining projects