DOCSLIB.ORG

Annotating Temporal Information in Clinical Narratives ⇑ Weiyi Sun A, , Anna Rumshisky B, Ozlem Uzuner C

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Annotating Temporal Information in Clinical Narratives ⇑ Weiyi Sun A, , Anna Rumshisky B, Ozlem Uzuner C View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Biomedical Informatics 46 (2013) S5–S12 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Methodological Review Annotating temporal information in clinical narratives ⇑ Weiyi Sun a, , Anna Rumshisky b, Ozlem Uzuner c a Department of Informatics, University at Albany, SUNY, 1400 Washington Ave., Draper 114B, Albany, NY 12222, United States b Department of Computer Science, University of Massachusetts, 198 Riverside St., Olsen Hall, Lowell, MA 01854, United States c Department of Information Studies, University at Albany, SUNY, 1400 Washington Ave., Draper 114A, Albany, NY 12222, United States article info abstract Article history: Temporal information in clinical narratives plays an important role in patients’ diagnosis, treatment and Received 21 May 2013 prognosis. In order to represent narrative information accurately, medical natural language processing Accepted 10 July 2013 (MLP) systems need to correctly identify and interpret temporal information. To promote research in this Available online 19 July 2013 area, the Informatics for Integrating Biology and the Bedside (i2b2) project developed a temporally anno- tated corpus of clinical narratives. This corpus contains 310 de-identified discharge summaries, with Keywords: annotations of clinical events, temporal expressions and temporal relations. This paper describes the pro- Natural Language Processing cess followed for the development of this corpus and discusses annotation guideline development, anno- Temporal Reasoning tation methodology, and corpus quality. Medical Informatics Corpus Building Ó 2013 Elsevier Inc. All rights reserved. Annotation 1. Introduction of temporally annotated clinical narratives [2] This corpus contains the clinical history and the hospital course sections of 310 de-iden- Electronic Medical Records (EMRs) contain significant amounts tified discharge summaries from Partners Healthcare and the Beth of unstructured narrative text, which can be turned into structured Israel Deaconess Medical Center, for a total of approximately data with help from automated medical language processing (MLP) 178,000 tokens. The corpus was annotated for three layers of infor- systems. Some sub-areas of MLP, such as de-identification and clin- mation: events, temporal expressions and their normalization, and ical concept (e.g. disorder, medication) extraction are well-studied. temporal relations. Our annotation scheme was adapted from Other areas, such as analysis of the temporal structures embedded TimeML [11]. More specifically, we annotate three types of tempo- in clinical texts, are less so [1]. Besides being a more complicated ral information: (1) EVENTs which represent the semantic events task, we believe that the lack of availability of manually annotated mentioned in the text that affect the patient’s clinical timeline; clinical corpora with temporal information also hindered the pro- (2) TIMEX3s that represent temporal expressions of date, times, gress of MLP in this area [2]. durations, and frequencies; and (3) TLINKs which represent the Temporal information in clinical narratives plays an important temporal relations between EVENTs and TIMEX3s. We refer to this role in medical decision-making and care assessment [3]. Some corpus as the i2b2 temporal relations corpus. examples of clinical applications that utilize temporal information In this paper, we present the process followed for the develop- include: diagnosis, prognosis and treatment decision support [3,4], ment of the i2b2 temporal relations corpus, including the creation time specific clinical information extraction [5–7], and time-re- of a temporal annotation scheme tailored to clinical narratives, the lated question answering [8–10]. These applications rely on tempo- methodology for applying the scheme to the i2b2 temporal rela- ral reasoning systems which extract temporal information from tions corpus, the evaluation of the resulting annotation quality, natural language, and perform temporal inference over the and a description of the resulting annotated corpus. We hope that extracted information. Temporal information in narrative texts in- this paper will: (1) inform the MLP researchers about the temporal cludes the events and the temporal expressions that appear in data preparation process, (2) guide the development of future clin- the text, as well as the temporal relations among them. ical temporal annotation guidelines, (3) caution against pitfalls and In order to develop and evaluate temporal reasoning systems, the issues often raised in the representation of temporal informa- we need clinical corpora annotated with temporal information. tion, and (4) share our solutions to these problems with the Given this need, the 2012 Informatics for Integrating Biology and community. the Bedside (i2b2) project provided the community with a corpus The remainder of this paper is organized as follows: Section 2 summarizes the related work in temporal representation and exist- ⇑ Corresponding author. ing temporally annotated corpora in the general as well as the MLP E-mail address: [email protected] (W. Sun). domains. Section 3 summarizes our annotation guidelines. Sec- 1532-0464/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jbi.2013.07.004 S6 W. Sun et al. / Journal of Biomedical Informatics 46 (2013) S5–S12 tion 4 presents our annotation methodology and procedures. The continuing; SLINKs indicate the subordinate relations between annotation quality evaluation and corpus statistics are presented EVENTs (e.g. the conditional or evidential relations between in Sections 5 and 6, respectively. two EVENTs) [22]. The TempEval [17–19] 2007 corpus applied a simplified 2. Related work TimeML annotation, and restricted the TLINK assignment to those (1) between EVENTs and document creation times, that 2.1. Temporal annotation is, the time stamp of the document creation; (2) between EVENTs/TIMEX3s in the same sentence and (3) between main A temporal representation scheme translates time-related infor- EVENTs (syntactically dominating verbs) in adjacent sentences. mation into a computer readable form to support temporal reason- The TempEval 2010 extended the 2007 annotations to multiple ing. Temporal annotation is a type of temporal representation that languages. TempEval 2012 used subsets of the TimeBank and focuses on interpreting time-related natural language information. AQUAINT corpora, as well as an automatically annotated Eng- Defining a temporal representation scheme is non-trivial in that it lish Gigaword corpus [23–25]. requires the specification of many fundamental assumptions about time [12]. This task becomes even more challenging when the tar- In the clinical domain, Galescu and Blaylock [21] applied an geted temporal information is embedded in natural language be- adaptation of TimeML guidelines to 40 discharge summaries [26]. cause time-related concepts are usually vaguely and implicitly Savova et al. [27] also described an adaptation of TimeML to clini- conveyed in free text [13,14]. For instance the verbal event ‘know’ cal narratives. The Clinical E-Science Framework (CLEF) project describes a continuous state, and the event ‘catch’ is instantaneous annotated a corpus of 167 clinical records for temporal relations [2]. [20]; however, they limited their annotations to intra-sentence The most prominent challenges in temporal annotation include: temporal relations and to the temporal relations between events (1) large search space in the assignment of TLINKs. Given the and document creation times. In addition to these full temporal EVENTs and TIMEX3s in one document, the theoretical search relation annotation schema, there are also annotations that focus space for TLINKs is (N-1)N/2 (N: total number of entities). (2) Mul- on some more specific temporal elements in the clinical narratives, tiple ways to represent the same set of TLINKs. TLINKs can be tran- such as conditions, temporal expressions [28,7,29]. These re- sitive (e.g. before or after) or equivalent (e.g. concurrence). For sources served as a good start at addressing the need for a tempo- example, let ‘<’ represent the before relation, ‘>’ for the after rela- rally annotated MLP corpus, and highlighted the need for tion, and ‘=’ for the concurrence relation, and for entities A, B, and comprehensive temporal annotations that can support the extrac- C, we have ‘A < B, B = C’. We can equivalently represent this rela- tion of the complete patient clinical timeline from narrative patient tion with ‘B > A, A < C, B = C’ among many other sets of TLINKs, records. We aimed to fill this gap. which give the annotators flexibility during annotation but whose equivalence is difficult to manually confirm. For this reason, we 3. Design of i2b2 annotation guidelines usually need to compute temporal closure when handling TLINKs. The temporal closure of a set of TLINKs is the set of minimal transi- We built our annotation guidelines on the following principles: tive relations that contains the original TLINKs. In the previous example, the temporal closure of ‘A < B, B = C’ contains the follow- (1) Ease of Use: the annotators should become proficient in the ing relations: ‘A < B, A < C, B > A, C > A, B = C, C = B’. (3) Conflict in task after a few short training sessions, and the human anno- TLINKs. The transitive and equivalent
Load more

Recommended publications
  • Temporal Information Extraction and Coreference Resolution in Electronic Health Records Julien Tourille
    Extracting Clinical Event Timelines : Temporal Information Extraction and Coreference Resolution in Electronic Health Records Julien Tourille To cite this version: Julien Tourille. Extracting Clinical Event Timelines : Temporal Information Extraction and Coref- erence Resolution in Electronic Health Records. Document and Text Processing. Université Paris Saclay (COmUE), 2018. English. NNT : 2018SACLS603. tel-01997223 HAL Id: tel-01997223 https://tel.archives-ouvertes.fr/tel-01997223 Submitted on 28 Jan 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Abstract Important information for public health is contained within electronic health records. Being able to extract this information automatically would allow to improve the daily medical care and to foster advances in clinical research. However, the large majority of it remains locked in documents written in natural language. Among all the clinical aspects that are of interest in these records, the patient timeline is one of the most important. Being able to retrieve clinical timelines would allow for a better understanding of some clinical phenomena such as disease progression and longitudinal ef- fects of medications. It would also allow to improve medical question answering and clinical outcome prediction systems. Accessing the clinical timeline is needed to evaluate the quality of the healthcare pathway by comparing it to clinical guidelines, and to highlight the steps of the pathway where specic care should be provided.
    [Show full text]
  • GUIR at Semeval-2016 Task 12: Temporal Information Processing for Clinical Narratives
    GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives Arman Cohan, Kevin Meurer and Nazli Goharian Information Retrieval Lab Department of Computer Science Georgetown University farman, [email protected], [email protected] Abstract Researchers have explored ways to extract temporal information from clinical text. Velupillai Extraction and interpretation of temporal et al. (2015) developed a pipeline based on information from clinical text is essential ClearTK3 and SVM with lexical features to extract for clinical practitioners and researchers. TIMEX3 and EVENT mentions. In I2b2 2012 SemEval 2016 Task 12 (Clinical TempEval) temporal challenge, all top performing teams 1 addressed this challenge using the THYME used a combination of supervised classification corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 and rule based methods for extracting temporal guidelines. We developed and evaluated information and relations (Sun et al., 2013). approaches for: extraction of temporal Besides THYME corpus, there have been other expressions (TIMEX3) and EVENTs; TIMEX3 efforts in clinical temporal annotation including and EVENT attributes; document-time works by Roberts et al. (2008), Savova et relations; and narrative container relations. al. (2009) and Galescu and Blaylock (2012). Our approach is based on supervised learning Previous work has also investigated extracting (CRF and logistic regression), utilizing temporal relations. Examples of these efforts various sets of syntactic, lexical and semantic features with addition of manually crafted include: classification by SVM (Chambers et rules. Our system demonstrated substantial al., 2007), Integer Linear Programming (ILP) for improvements over the baselines in all the temporal ordering (Chambers and Jurafsky, 2008), tasks.
    [Show full text]
  • Survey of Temporal Information Extraction
    J Inf Process Syst, Vol.15, No.4, pp.931~956, August 2019 ISSN 1976-913X (Print) https://doi.org/10.3745/JIPS.04.0129 ISSN 2092-805X (Electronic) Survey of Temporal Information Extraction Chae-Gyun Lim*, Young-Seob Jeong**, and Ho-Jin Choi* Abstract Documents contain information that can be used for various applications, such as question answering (QA) system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary to develop a method of extracting such information from the documents written in a form of natural language. There are several kinds of the information (e.g., temporal information, spatial information, semantic role information), where different kinds of information will be extracted with different methods. In this paper, the existing studies about the methods of extracting the temporal information are reported and several related issues are discussed. The issues are about the task boundary of the temporal information extraction, the history of the annotation languages and shared tasks, the research issues, the applications using the temporal information, and evaluation metrics. Although the history of the tasks of temporal information extraction is not long, there have been many studies that tried various methods. This paper gives which approach is known to be the better way of extracting a particular part of the temporal information, and also provides a future research direction. Keywords Annotation Language, Temporal Information, Temporal Information Extraction 1. Introduction Documents are used to deliver information to the readers. In the past, readers were human, but computers are becoming a new class of readers. Computers can collect information much faster than humans can, and are capable of storing much more information than humans are.
    [Show full text]
  • A Hybrid System for Temporal Information Extraction from Clinical Text Buzhou Tang,1,2 Yonghui Wu,1 Min Jiang,1 Yukun Chen,3 Joshua C Denny,3,4 Hua Xu1,3
    Research and applications A hybrid system for temporal information extraction from clinical text Buzhou Tang,1,2 Yonghui Wu,1 Min Jiang,1 Yukun Chen,3 Joshua C Denny,3,4 Hua Xu1,3 1School of Biomedical ABSTRACT than general English texts due to lack of formalism Informatics, The University of Objective To develop a comprehensive temporal in writing quality. Texas Health Science Center at Houston, Houston, Texas, USA information extraction system that can identify events, To accelerate TIE research in the medical 2Department of Computer temporal expressions, and their temporal relations in domain, the 2012 Informatics for Integrating Science, Harbin Institute of clinical text. This project was part of the 2012 i2b2 Biology and Beside (i2b2) clinical NLP challenge Technology Shenzhen Graduate clinical natural language processing (NLP) challenge on focused on extraction of temporal information School, Shenzhen, Guangdong, temporal information extraction. from hospital discharge summaries.4 The challenge China 3Department of Biomedical Materials and methods The 2012 i2b2 NLP consists of three sub-tasks: (1) clinical event extrac- Informatics, Vanderbilt challenge organizers manually annotated 310 clinic tion with relevant attributes (eg, polarity) in clinic University, School of Medicine, notes according to a defined annotation guideline: a text; (2) temporal expression extraction, which Nashville, Tennessee, USA training set of 190 notes and a test set of 120 notes. requires both identification and normalization of 4Department of Medicine,
    [Show full text]
  • Reconstructing the Patient's Natural History from Electronic
    Title: Reconstructing the Patient’s Natural History from Electronic Health Records Authors: 1. Corresponding Author Name: Marjan Najafabadipour Affiliation: Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain Postal Address: Centro de Tecnología Biomédica (CTB), Campus de Montegancedo, Universidad Politécnica de Madrid, M-40, Pozuelo de Alarcón, Madrid, 28223 Email: [email protected] Mobile: +34612247147 ORCID: 0000-0002-1428-9330 2. Coauthor Name: Massimiliano Zanin Affiliation: Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain Email: [email protected] ORCID: 0000-0002-5839-0393 3. Coauthor Name: Alejandro Rodríguez-González Affiliation: Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain Email: [email protected] ORCID: 0000-0001-8801-4762 4. Coauthor Name: Maria Torrente Affiliation: Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain Email: [email protected] 5. Coauthor Name: Beatriz Nuñez García Affiliation: Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain Email: [email protected] 6. Coauthor Name: Juan Luis Cruz Bermudez Affiliation: Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain Email: [email protected] 7. Coauthor Name: Mariano Provencio Affiliation: Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain Email: [email protected] 8. Coauthor Name: Ernestina Menasalvas Affiliation: Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain Email: [email protected] ORCID: 0000-0002-5615-6798 1 Abstract. The automatic extraction of a patient’s natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient’s medical care, this information can only be fully understood when analyzed in a temporal context.
    [Show full text]
  • Temporal Information Extraction Extracting Events and Temporal Expressions a Literature Survey
    Temporal Information Extraction Extracting Events and Temporal Expressions A Literature Survey Naman Gupta 133050012 June 18, 2015 Chapter 1 Introduction Information on web is increasing at infinitum. There exists plethora of data on World Wide Web (WWW) in various electronic and digital form. Thus, web has become an unstructured global area where information even if available, cannot be directly used for desired applications. One is often faced with an information overload and demands for some automated help. Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine- readable documents by means of Text Mining and Natural Language Processing (NLP) techniques. Extracted structured information can be used for variety of enterprise or personal level task of varying complexity. In this survey report we will discuss literature related to temporal expressions and event extraction. 1.1 Temporal Expression Recognition Temporal Expression Recognition (TER) is the process of locating phrases that denote temporal infor- mation. Temporal expressions may be an expressed point in time, a duration or a frequency. These expressions can be used in information extraction and question-answering to (a) answer time-specific queries, (b) arrange information in a chronological manner, etc. Research in TER mostly exists in news domain text, arguably because of availability of large corpora and presence of temporal expressions in news documents. In recent times, TER has also been applied to other domains like medical (Sohn et al., 2013), (Jindal and Roth, 2013), (Xu et al., 2013), (Roberts et al., 2013). Initially, temporal expressions were considered a type of named entities and their identification was part of the named entity recognition task.
    [Show full text]
  • Ann K. Irvine. Natural Language Processing and Temporal Information Extraction in Emergency Department Triage Notes. a Master’S Paper for the M.S
    Ann K. Irvine. Natural Language Processing and Temporal Information Extraction in Emergency Department Triage Notes. A Master’s Paper for the M.S. in I.S degree. April, 2008. 56 pages. Advisor: Stephanie W. Haas Electronic patient records, including the Emergency Department (ED) Triage Note (TN), provide a rich source of textual information. Processing clinical texts to create important pieces of structured information will be useful to clinicians treating patients, clinicians in training, and researchers and practitioners in biosurveillance. This work applies natural language processing (NLP) and information extraction (IE) techniques to the TN genre of text. In particular, it presents the Triage Note Temporal Information Extraction System (TN-TIES), which combines a shallow parser, machine learned classifiers, and hand- written rules to identify, extract, and interpret temporal information in TNs in preparation for the automatic creation of a timeline of events leading up to a patient’s visit to the ED. The success of TN-TIES suggests that NLP and IE techniques are appropriate for the genre and that the automatic production of a timeline of TN events is a realistic application. Headings: Medical informatics – Technological innovations Medical records – Data processing Natural Language Processing Semantics – Data processing Temporal event analysis Text processing (Computer Science) NATURAL LANGUAGE PROCESSING AND TEMPORAL INFORMATION EXTRACTION IN EMERGENCY DEPARTMENT TRIAGE NOTES by Ann K. Irvine A Master’s paper submitted to the faculty of the School of Information and Library Science of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Science in Information Science .
    [Show full text]
  • Linguistic Support for Protest Event Data Collection
    Universitat Autonoma` de Barcelona TESI DOCTORAL Linguistic Support for Protest Event Data Collection Dirigida per: Autor(a): Dr. Xavier Blanco Escoda, Vera Danilova, M. Sc. Dr. Mikhail Alexandrov Doctorat en Lleng¨uesi Cultures Rom`aniques Department de Filologia Francesa i Rom`anica Facultat de Filosofia i Lletres September, 2015 UNIVERSITAT AUTONOMA` DE BARCELONA Abstract Facultat de Filosofia i Lletres Department de Filologia Francesa i Rom`anica Linguistic Support for Protest Event Data Collection Vera Danilova, M. Sc. ii The development of approaches to structuring chaotic information streams coming from the web led to the emergence of a new powerful instrument for the study of col- lective human behaviour: Internet Sociology. The variety of user-generated data, both quantitative and qualitative, allowed for a research from a different perspective, without the need of traditional questionnaires. Sociologists benefit from the data coming from news, blogs, social networks, comments, etc. to get an insight into the relation between online and offline collective human behaviour trends. Social science has a long history of research on the protest activity. Today, the sys- tematic quantitative study of this phenomenon acquires importance in the light of the world-famous events, such as Maidan, Gezi Park protests, "Charlie Hebdo" and others. Protests are being reported almost daily by the news and are constantly reverberating across the social media. Today’s news are global and discuss similar events in different languages producing lots of duplicate messages and, sometimes, contradictory descrip- tions. Reports are created by either individual authors or agencies and vary in their textual representation. Despite the fact that news media credibility is questionable, it is still the most used data source in the context of protest events collection.
    [Show full text]
  • Modern Clinical Text Mining: a Guide and Review
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 3 February 2021 doi:10.20944/preprints202010.0649.v2 Modern Clinical Text Mining: A Guide and Review Bethany Percha1;2 1Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10025; email: [email protected] 2Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10025 Xxxx. Xxx. Xxx. Xxx. YYYYAA Keywords https://doi.org/10.1146/((please add text mining, natural language processing, electronic health record, article doi)) clinical text, machine learning Abstract Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. How- ever, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has ad- vanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new meth- ods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, opera- tional analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable tech- nical advances and the practical realities of implementation at health systems and in industry. 1 © 2021 by the author(s). Distributed under a Creative Commons CC BY license.
    [Show full text]