Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports

The sheer amount of information about potential adverse drug events publishedin medical case reports pose major challenges for drug safety experts toperform timely monitoring. Efficient strategies for identification andextraction of information about potential adverse drug events fromfree‐text resources are needed to support pharmacovigilance researchand pharmaceutical decision making. Therefore, this work focusses on theadaptation of a machine learning‐based system for the identificationand extraction of potential adverse drug event relations from MEDLINE casereports. It relies on a high quality corpus that was manually annotatedusing an ontology‐driven methodology. Qualitative evaluation of thesystem showed robust results. An experiment with large scale relationextraction from MEDLINE delivered under‐identified potential adversedrug events not reported in drug monographs. Overall, this approach providesa scalable auto‐assistance platform for drug safety professionals toautomatically collect potential adverse drug events communicated asfree‐text data.

/pdf/extraction-of-potential-adverse-drug-events-from-medical-1yvazzecxl.pdf

Extraction of potential adverse drug events from medical case reports

https://eprints.whiterose.ac.uk/10186/2/Roberts1.pdf

Building a semantically annotated corpus of clinical texts

The EU-ADR corpus

Rule-based information extraction from patients' clinical data

The Clinical E-Science Framework (CLEF) project is building a framework for the capture, integration and presentation of clinical information: for clinical research, evidence-based health care and genotype-meets-phenotype informatics. A significant portion of the information required by such a framework originates as text, even in EHR-savvy organizations. CLEF uses Information Extraction (IE) to make this unstructured information available. An important part of IE is the identification of semantic entities and relationships. Typical approaches require human annotated documents to provide both evaluation standards and material for system development. CLEF has a corpus of clinical narratives, histopathology reports and imaging reports from 20 thousand patients. We describe the selection of a subset of this corpus for manual annotation of clinical entities and relationships. We describe an annotation methodology and report encouraging initial results of inter-annotator agreement. Comparisons are made between different text sub-genres, and between annotators with different skills.

/pdf/the-clef-corpus-semantic-annotation-of-clinical-text-4k9huaantr.pdf

The CLEF Corpus: Semantic Annotation of Clinical Text

In this paper we discuss the design, implementation, and use of Termino, a large scale terminological resource for text processing. Dealing with terminology is a difficult but unavoidable task for language processing applications, such as Information Extraction in technical domains. Complex, heterogeneous information must be stored about large numbers of terms. At the same time term recognition must be performed in realistic times. Termino attempts to reconcile this tension by maintaining a flexible, extensible relational database for storing terminological information and compiling finite state machines from this database to do term lookup. While Termino has been developed for biomedical applications, its general design allows it to be used for term processing in any domain.

A Large Scale Terminology Resource for Biomedical Text Processing

Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is increasingly available in the electronic scientific literature. However, publishers are not text mining specialists, nor typically are the end user scientists who consume their products. This situation suggests a web services based solution, where text mining specialists process the literature obtained from publishers and make their results available to remote consumers (research scientists). In this paper we discuss the integration of web services and text mining within the domain of scientific publishing and explore the strengths and weaknesses of three generic architectural designs for delivering text mining web services. We argue for the superiority of one of these and demonstrate its viability by reference to an application designed to provide access to the results of text mining over the PubMed database of scientific abstracts.

Web Service Architectures for Text Mining: An Exploration of the Issues via an E-Science Demonstrator

This paper discusses the design and implementation of Termino, a large-scale terminological resource for text processing. Dealing with terminology is a difficult but unavoidable task for natural language processing applications, such as information extraction in technical domains. Complex, heterogeneous information must be stored about large numbers of terms. At the same time term recognition must be performed in realistic times. Termino attempts to reconcile this tension by maintaining a flexible, extensible relational database for storing terminological information and compiling finite state machines from this database to do term recognition.

/pdf/a-large-scale-resource-for-storing-and-recognizing-technical-26j3757s2x.pdf

A Large-Scale Resource for Storing and Recognizing Technical Terminology.

Workflows are useful ways to support scientific researchers in carrying out repetitive analytical tasks on digital information. Web services can provide a useful implementation mechanism for workflows, particularly when they are distributed, i.e., where some of the data or processing resources are remote from the scientist initiating the workflow. While many scientific workflows primarily involve operations on structured or numerical data, all interpretation of results is done in the context of related work in the field, as reflected in the scientific literature. Text mining technology can assist in automatically building helpful pathways into the relevant literature as part of a workflow in order to support the scientific discovery process. We demonstrate how these three technologies - workflows, text mining, and Web services - can be fruitfully combined in order to support bioinformatics researchers investigating the genetic basis of two physiological disorders - Graves' disease and Williams syndrome.

/pdf/integrating-text-mining-into-distributed-bioinformatics-2zoyy5rfh0.pdf

Neil Davis

Papers

The CLEF Corpus: Semantic Annotation of Clinical Text

A Large Scale Terminology Resource for Biomedical Text Processing

Web Service Architectures for Text Mining: An Exploration of the Issues via an E-Science Demonstrator

A Large-Scale Resource for Storing and Recognizing Technical Terminology.

Integrating text mining into distributed bioinformatics workflows: a Web services implementation