Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases

Rodríguez‐González, Alejandro; Martínez-Romero, Marcos; Costumero, Roberto; Wilkinson, Mark D.; Menasalvas, Ernestina

doi:10.1007/978-3-319-19776-0_9

Cited by 16 publications

(15 citation statements)

References 9 publications

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“…As an extension of a previous work [7] we present the resutls of using a Apache cTAKES to retrieve clinical terms from MedlinePlus texts. We have used the same set of 30 infectious diseases.…”

Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

“…The idea of this use case is to show a comparative in accuracy regarding the extraction of generalist medical terms that only affect to terms used in the diagnosis context. As has been outlined in [7] only those semantic types that belong to the classes related with diagnostic elements were used to filter. The experiment was performed using our framework in which Apache cTAKES is used as NER.…”

Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

“…The experiment was performed using our framework in which Apache cTAKES is used as NER. We have manually analyzed the results and made a comparison between MetaMap ( [7]) and Apache cTAKES.…”

Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

“…In the first case study we have applied two well-known NLP tools named MetaMap and Apache cTAKES to extract medical diagnosis terms (symptoms, signs, laboratory procedures and tests and diagnostic procedures) as an extension of a previous work [7] to compare the accuracy of both methods. The tools were applied against a set of 30 infectious diseases provided by Medline Plus.…”

Section: Introductionmentioning

confidence: 99%

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Clinical Narrative Analytics Challenges

et al. 2016

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Abstract. Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different ser vices, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and val idate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts pro vided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish.

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Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

Section: Extracting Clinical Terms From Medical Textsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Narrative Analytics Challenges

et al. 2016

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“…In this research, we have conceived, tested and evaluated a new way of extracting relevant medical diagnostic terms from a set of online MedLine Plus articles about infectious diseases as an extension of a previously published research [9]. We have developed a prototype capable of crawling the HTML code of the Web pages in order to extract all relevant diagnosis-related content (symptoms, signs and diagnostic tests).…”

Section: Introductionmentioning

confidence: 99%

Extracting Diagnostic Knowledge from MedLine Plus: A Comparison between MetaMap and cTAKES Approaches

Rodríguez‐González

Costumero

Martínez-Romero

et al. 2018

CBIO

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Abstract:The development of diagnostic decision support systems (DDSS) requires having a reliable and consistent knowledge base about diseases and their symptoms, signs and diagnostic tests. Physicians are typically the source of this knowledge, but it is not always possible to obtain all the desired information from them. Other valuable sources are medical books and articles describing the diagnosis of diseases, but again, extracting this information is a hard and timeconsuming task. In this paper we present the results of our research, in which we have used Web scraping, natural language processing techniques, a variety of publicly available sources of diagnostic knowledge and two widely known medical concept identifiers, MetaMap and cTAKES, to extract diagnostic criteria for infectious diseases from MedLine Plus articles. A performance comparison of MetaMap and cTAKES is also presented.

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