Ontology-based time information representation of vaccine adverse events in VAERS for temporal analysis

Tao, Cui; He, Yongqun; Yang, Hannah; Chute, Christopher G.

doi:10.1186/2041-1480-3-13

Cited by 17 publications

(18 citation statements)

References 10 publications

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“…A comprehensive system for extracting temporal information from clinical texts was proposed by Tang et al [ 20 ]. Tao et al presented a method for identifying temporal representations of vaccine adverse events using ontology for temporal analysis [ 21 ]. Li & Patrick [ 22 ] addressed a statistical model using linguistic, contextual and semantic features for extracting temporal expressions from an extremely noisy clinical corpus.…”

Section: Introductionmentioning

confidence: 99%

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

Hao

Pan

et al. 2018

BMC Med Inform Decis Mak

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BackgroundTemporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts.MethodsA novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple , TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N.ResultsBased on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries.ConclusionsAn automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.

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Section: Introductionmentioning

confidence: 99%

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

Hao

Pan

et al. 2018

BMC Med Inform Decis Mak

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“…Their studies show that vaccine formulation and administration route can independently or collaboratively affect the outcomes of vaccine-induced host responses. Tao et al introduce a novel method for using ontologies (including Time Event Ontology or TEO, VO, and Ontology of Adverse Events or OAE) and semantic web technologies to represent and reason temporal information and relations for post-vaccination events reported in the US FDA/CDC Vaccine Adverse Event Reporting System (VAERS) [2]. Effective analyses of time trends for post-vaccine adverse events (AEs) can enhance clinical research in different areas such as vaccine safety analyses, causality assessments, and retrospective studies.…”

Section: Summary Of Selected Papers In the Thematic Issuementioning

confidence: 99%

A 2012 Workshop: Vaccine and Drug Ontology in the Study of Mechanism and Effect (VDOSME 2012)

Toldo

Burns

et al. 2012

J Biomed Sem

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Vaccines and drugs have contributed to dramatic improvements in public health worldwide. Over the last decade, there have been efforts in developing biomedical ontologies that represent various areas associated with vaccines and drugs. These ontologies combined with existing health and clinical terminology systems (e.g., SNOMED, RxNorm, NDF-RT, MedDRA, VO, OAE, and AERO) could play significant roles on clinical and translational research. The first “Vaccine and Drug Ontology in the Study of Mechanism and Effect” workshop (VDOSME 2012) provided a platform for discussing problems and solutions in the development and application of biomedical ontologies in representing and analyzing vaccines/drugs, vaccine/drug administrations, vaccine/drug-induced immune responses (including positive host responses and adverse events), and similar topics. The workshop covered two main areas: (i) ontologies of vaccines, of drugs, and of studies thereof; and (ii) analysis of administration, mechanism and effect in terms of representations based on such ontologies. Six full-length papers included in this thematic issue focus on ontology representation and time analysis of vaccine/drug administration and host responses (including positive immune responses and adverse events), vaccine and drug adverse event text mining, and ontology-based Semantic Web applications. The workshop, together with the follow-up activities and personal meetings, provided a wonderful platform for the researchers and scientists in the vaccine and drug communities to demonstrate research progresses, share ideas, address questions, and promote collaborations for better representation and analysis of vaccine and drug-related terminologies and clinical and research data.

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“…Chang et al [ 18 ] applied a method combining regular expression rules, compositional rules, and filtering rules to identify TEs in text. For the purpose of temporal analysis, Tao et al [ 19 ] proposed an ontology-based method for temporal information representation of vaccine adverse events. A comprehensive approach consisting of regular expression, pattern matching, and machine learning was developed by Sohn et al [ 20 ] for temporal information processing.…”

Section: Introductionmentioning

confidence: 99%

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

Pan¹,

Chen²,

Weng³

et al. 2020

JMIR Med Inform

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Background Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. Objective The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. Methods TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. Results The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. Conclusions This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.

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Ontology-based time information representation of vaccine adverse events in VAERS for temporal analysis

Cited by 17 publications

References 10 publications

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

A 2012 Workshop: Vaccine and Drug Ontology in the Study of Mechanism and Effect (VDOSME 2012)

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

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