Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature

Alnazzawi, Noha; Thompson, Paul; Ananiadou, Sophia

doi:10.3115/v1/w14-1110

Cited by 15 publications

(10 citation statements)

References 17 publications

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“…In this paper, we extend upon our previously reported work [ 20 ], which was focussed only on the construction of PhenoCHF. Specifically, using an augmented version of the corpus, we have carried out experiments to train systems to recognise phenotypic information automatically, employing different ML algorithms and feature sets.…”

Section: Introductionmentioning

confidence: 89%

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Alnazzawi

Thompson

Batista-Navarro

et al. 2015

BMC Med Inform Decis Mak

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BackgroundPhenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text.MethodsTo stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013.ResultsEvaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set.ConclusionsPhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

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Section: Introductionmentioning

confidence: 89%

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Alnazzawi

Thompson

Batista-Navarro

et al. 2015

BMC Med Inform Decis Mak

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“…To capture and represent phenotypic information, we developed a typology of clinical concepts (Table 1 ) taking inspiration from the definition of COPD phenotypes previously proposed [ 2 ], i.e., “a single or combination of disease attributes that describe differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, rate of disease progression, or death).” After reviewing the semantic representations used in previous clinical annotation efforts, we decided to adapt and harmonise concept types from the annotation schemes applied to the 2010 i2b2/VA Shared Task data set [ 42 ] and the PhenoCHF corpus [ 43 ]. In the former, concepts of interest were categorised into broad types of problem, treatment and test/measure.…”

Section: Methodsmentioning

confidence: 99%

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows

Batista-Navarro

Rak

et al. 2015

J Biomed Sem

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BackgroundChronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often “hidden” within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients.MethodsA corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents.ResultsWhen evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors.ConclusionsWe describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0004-6) contains supplementary material, which is available to authorized users.

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“…information from complementary text sources. For example, PhenoCHF [18] and COPD [19,20] are collections of EHRs and research articles obtained from the literature. Both corpora have proven to be very useful and have been used to develop TM tools to extract and integrate phenotype information.…”

Section: Plos Onementioning

confidence: 99%

“…To ensure the relevance of the scheme to our research goals, we worked closely with a medical expert who is an internal medicine doctor and functioned as a guide and judge throughout the annotation process. After the analysis of the relevant documents of the corpus (i.e., EHRs and tweets) by the medical experts, in conjunction with a review of comparable domain-specific schemata and guidelines such as COPD [19,20], PhenoCHF [18] and i2b2 [43], the schema shown in Fig 2 was established by taking into account our chosen focus of annotating the complications associated with hypertension and diabetes. The medical doctor was asked to determine the entity types relevant to the task (explained in Table 2).…”

Section: B Annotation Scheme and Guidelinesmentioning

confidence: 99%

Building a semantically annotated corpus for chronic disease complications using two document types

Alnazzawi¹

2021

PLoS ONE

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Narrative information in electronic health records (EHRs) contains a wealth of information related to patient health conditions. In addition, people use Twitter to express their experiences regarding personal health issues, such as medical complaints, symptoms, treatments, lifestyle, and other factors. Both genres of text include different types of health-related information concerning disease complications and risk factors. Knowing detailed information about controlling disease risk factors has a great impact on modifying these risks and subsequently preventing disease complications. Text-mining tools provide efficient solutions to extract and integrate vital information related to disease complications hidden in the large volume of the narrative text. However, the development of text-mining tools depends on the availability of an annotated corpus. In response, we have developed the PrevComp corpus, which is annotated with information relevant to the identification of disease complications, underlying risk factors, and prevention measures, in the context of the interaction between hypertension and diabetes. The corpus is unique and novel in terms of the very specific topic in the biomedical domain and as an integration of information from both EHRs and tweets collected from Twitter. The annotation scheme was designed with guidance by a domain expert, and two further domain experts performed the annotation, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.60 and 0.75 for EHRs and tweets, respectively.

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Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature

Cited by 15 publications

References 17 publications

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows

Building a semantically annotated corpus for chronic disease complications using two document types

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