Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

V-Model: a new perspective for EHR-based phenotyping

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background Narrative resources in electronic health records make clinical phenotyping study difficult to achieve. If a narrative patient history can be represented in a timeline, this would greatly enhance the efficiency of information-based studies. However, current timeline representations have limitations in visualizing narrative events. In this paper, we propose a temporal model named the V-Model which visualizes clinical narratives into a timeline. Methods We developed the V-Model which models temporal clinical events in v-like graphical structure. It visualizes patient history on a timeline in an intuitive way. For the design, the representation, reasoning, and visualization (readability) aspects were considered. Furthermore, the unique graphical notation helps to find hidden patterns of a specific patient group. For evaluation, we verified our distinctive solutions, and surveyed usability. The experiments were carried out between the V-Model and a conventional timeline model group. Eighty medical students and physicians participated in this evaluation. Results The V-Model was proven to be superior in representing narrative medical events, provide sufficient information for temporal reasoning, and outperform in readability compared to a conventional timeline model. The usability of the V-Model was assessed as positive. Conclusions The V-Model successfully resolves visualization issues of clinical documents, and provides better usability compared to a conventional timeline model

Incidence of medication-related harm in older adults following hospital discharge: a systematic review

Objectives To determine the incidence, severity, preventability and risk factors for medication-related harm (MRH)in community-dwelling older adults following hospital discharge. Design: Systematic review Setting: A search of Medline, EMBASE, CINAHL, and the Cochrane Library was undertaken without time restrictions Participants Older adults (average age 65 years) participating in observational studies investigating adverse drug reactions (ADR) or adverse drug events (ADE)post-discharge within a defined follow-up period Measurements The abstracts of all articles were initially screened by one author to exclude obviously irrelevant articles. The remaining articles were independently screened by two authors for inclusion. Data extraction,including study characteristics, MRH incidence and risk factors, and, critical appraisal was performed by two authors independently, and verified by a third reviewer. Disagreements were resolved through discussion. Results Out of 584 potentially relevant articles, eight studies met our inclusion criteria; five North American and three European. Most of the included studies were of moderate quality. There was a wide range in MRH incidence, from 0.4% to 51.2% of patients, and 35% to 59% of MRH was preventable. The MRH incidence within 30 days post-discharge ranges from 167 to 500 events per 1000 patients discharged (17-51% of patients. Substantial methodological heterogeneity exists across multiple domains of the studies, including ADR and ADE definitions, characteristics of recruited populations, the follow-up duration post-discharge, and data collection. Conclusions Medication-related harm is common following hospital discharge in older adults. However, a clear understanding of the epidemiology is hampered by methodological inconsistencies between studies and a paucity of data on risk factors. There is need for international consensus on conducting and reporting MRH studies.Data from large, multicentre studies examining a range of biopsychosocial risk factors could add insight to this important area of patient safety

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

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

Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models