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Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications

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Abstract

Electronic health records (EHR) contain patients’ health information in varied formats such as clinical reports written in natural language, X-rays, MRI, case/discharge-summary, etc. One of its essential constituents is clinical narratives which contain significant clinical findings of a patient. Since the clinical narratives are stored in natural language, clinical evidence, significant findings, and other observations written in it by doctors remain locked. This free text information is incomprehensible by machines while processing EHRs in healthcare applications such as Clinical Decision Support System. The proposed work, Clinical Narratives to Knowledge Graph (N2K) Mapper algorithm, is an effort to map clinical narratives to a Knowledge Graph (KG) so that important clinical details as recommended by doctors can be used effectively by healthcare applications. The KG is defined by an ontological semantic meta-data called Healthcare Ontology. The N2K Mapper algorithm uses natural language processing to parse the clinical narratives and recognizes medicinal vocabulary using named entity recognition and various existing biomedical ontologies. This semantically retrieved information is then mapped onto a KG. Besides enriching the KG from clinical narratives, the algorithm also augments the biomedical ontologies with new medicinal vocabulary leading to sustainable semantic structure for future references. An experimental study was performed on Chest X-ray radiology reports taken from the Medical Information Mart for Intensive Care (MIMIC)-III dataset. It showed that the N2K Mapper was able to find and recognize approximately 80% of the total identified entities in the existing biomedical ontologies. The remaining 20% were augmented in the biomedical ontologies with an expert’s assistance. The N2K Mapper showed an accuracy of 81.5%, precision of 78.5%, recall of 85.07%, and F1-score of 78.97%.

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Data Availability

The data that support the findings of this study are available from [35] (https://physionet.org/content/mimiciii/1.4/), but restrictions apply to the availability of these data, which were used under license for the current study.

Notes

  1. https://www.ebi.ac.uk/ols/ontologies/symp

  2. https://disease-ontology.org/.

  3. http://radlex.org/.

  4. https://www.ebi.ac.uk/ols/ontologies/fma.

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Authors and Affiliations

  1. Keshav Mahavidyalaya, University of Delhi, Delhi, 110034, India

    Anjali Thukral

  2. Department of Computer Science, University of Delhi, Delhi, 110007, India

    Shivani Dhiman & Punam Bedi

  3. Maulana Azad Medical College, University of Delhi, Delhi, 110002, India

    Ravi Meher

Authors
  1. Anjali Thukral
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  2. Shivani Dhiman
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  3. Ravi Meher
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  4. Punam Bedi
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Correspondence to Shivani Dhiman.

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Thukral, A., Dhiman, S., Meher, R. et al. Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications. Int. j. inf. tecnol. 15, 53–65 (2023). https://doi.org/10.1007/s41870-022-01145-y

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