Rule-Based Method for Automatic Medical Concept Extraction from Unstructured Clinical Text

Sahu, Ruchi

doi:10.1007/978-981-10-8633-5_27

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Previously, various traditional rule-based methods have been designed for NLP and text-mining research for unstructured clinical documents. Different tools such as MetaMap were introduced to identify clinical concepts from unstructured clinical documents that utilized UMLS terminology [11]. MetaMap 2013v2 experimented on the i2b2 2010 clinical dataset with the NLM 2013AB database and obtained low precision (47.3%) and recall (36%) scores.…”

Section: Related Workmentioning

confidence: 99%

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Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation

Abbas

Afzal

Hussain

et al. 2021

IJERPH

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Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automatically extracts clinical concepts from unstructured narratives with higher accuracy and transparency. The proposed system is a pipelined approach, capable of recognizing clinical concepts of three types, problem, treatment, and test, in the dataset collected from a published repository as a part of the I2b2 challenge 2010. The system’s performance is compared with that of three existing systems: Quick UMLS, BIO-CRF, and the Rules (i2b2) model. Compared to the baseline systems, the average F1-score of 72.94% was found to be 13% better than Quick UMLS, 3% better than BIO CRF, and 30.1% better than the Rules (i2b2) model. Individually, the system performance was noticeably higher for problem-related concepts, with an F1-score of 80.45%, followed by treatment-related concepts and test-related concepts, with F1-scores of 76.06% and 55.3%, respectively. The proposed methodology significantly improves the performance of concept extraction from unstructured clinical narratives by exploiting the linguistic and lexical semantic features. The approach can ease the automatic annotation process of clinical data, which ultimately improves the performance of supervised data-driven applications trained with these data.

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Section: Related Workmentioning

confidence: 99%

“…Rules have been defined to map semantic information for concept classification. This methodology gains a precision score of 70% and an average recall of 60% [11], but the recall still needs to be improved to avoid missing information. A set of rules was constructed in [22] by extracting medical concepts from annotated training data.…”

Section: Rule-based Approachmentioning

confidence: 99%