Clinical Narrative Analytics Challenges

Menasalvas, Ernestina; Rodríguez‐González, Alejandro; Costumero, Roberto; Ambit, Héctor; Gonzalo, Consuelo

doi:10.1007/978-3-319-47160-0_2

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…C-liKES is a framework that has been developed on top of Apache UIMA, and which has been based on a legacy system named H2A [6]. The framework is a text-mining system that has been developed to ingest clinical information in free-text format; and to yield a structured output that can both support complex queries and be used for applying more in-depth analytics such as the application of machine learning techniques.…”

Section: Methodsmentioning

confidence: 99%

Profiling Lung Cancer Patients Using Electronic Health Records

et al. 2018

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If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study.

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Section: Methodsmentioning

confidence: 99%

Profiling Lung Cancer Patients Using Electronic Health Records

et al. 2018

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“…It achieves recall of 90% and precision of 89%. Ernestina Menasalvas et al [11] analyze several tools and frameworks to extract medical entities by applying NLP with NER process ,P.O. El Guedj et al [12] build Chart parser which is used to analyze large medical corpora.Nuala A. Bennett et al [13] extract noun phrase from Medline using general parser.…”

Section: Introductionmentioning

confidence: 99%

Constituency Parser for Clinical Narratives using NLP

Kedawat*,

Senthil

2020

IJITEE

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Clinical parsing is useful in medical domain .Clinical narratives are difficult to understand as it is in unstructured format .Medical Natural language processing systems are used to make these clinical narratives in readable format. Clinical Parser is the combination of natural language processing and medical lexicon .For making clinical narrative understandable parsing technique is used .In this paper we are discussing about constituency parser for clinical narratives, which is based on phrase structured grammar. This parser convert unstructured clinical narratives into structured report. This paper focus on clinical sentences which is in unstructured format after parsing convert into structured format. For each sentence recall ,precision and bracketing f- measure are calculated .

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“…One of the main challenges currently faced by health professionals in their clinical practice is to transform the large amount of available clinical information, generally stored in textual form, into useful knowledge that allows carrying out daily clinical decision‐making processes. However, the automatization of this process is not an easy task mainly due to the special characteristics that clinical information gathers: low degree of terminological standardization, high ambiguity, complex vocabulary, short sentences that may contain grammatical errors, excessive use of acronyms, combination of structured and unstructured data, and texts usually written in narrative form 1 …”

Section: Introductionmentioning

confidence: 99%

A semantically enriched text mining system for clinical decision support

Luque

Luna

Ventura

2020

Computational Intelligence

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Existing systems to support decision‐taking process based on textual information of clinical reports are insufficient. Currently, there are few systems that unify different subtasks in a single and user‐friendly framework, easing therefore the clinical work by automating complex and arduous tasks such as the detection of clinical alerts as well as clinical information coding. To address this issue, MiNerDoc is proposed as a new text mining (TM) system whose main objective is to support clinical decision‐taking processes by analyzing textual clinical reports in a unified framework. MiNerDoc is a really alluring TM system that includes two relevant tasks in the medical field, that is, detection of risk factors according to five medical entities (disease, pharmacologic, region/part body, procedure/test, and finding/sign) and automatic prediction of standardized diagnostic codes (MeSH descriptors associated with diseases). MiNerDoc integrates a combination of techniques from the TM discipline along with the terminological and semantic enrichment provided by the MetaMap tool and UMLS metathesaurus. Some study cases as well as a wide experimental analysis on real clinical reports have been carried out to demonstrate the effectiveness and promising performance of MiNerDoc on two different tasks, that is, medical entities recognition (FMeasure 81.54%) and diagnostic classification (FMeasuremic 81.04%).

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Clinical Narrative Analytics Challenges

Cited by 14 publications

References 24 publications

Profiling Lung Cancer Patients Using Electronic Health Records

Profiling Lung Cancer Patients Using Electronic Health Records

Constituency Parser for Clinical Narratives using NLP

A semantically enriched text mining system for clinical decision support

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