Medical concept extraction: A comparison of statistical and semantic methods

Khin, Nyein Pyae Pyae; Lynn, Khin Thidar

doi:10.1109/snpd.2017.8022697

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…It involves identifying lemmas, which are the base forms of words. For example, "better" would be converted to "good" by a lemmatization algorithm [11].…”

Section: S-5: Lemmatizationmentioning

confidence: 99%

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

Ranjan,

Tiwari,

Sattar

et al. 2024

RAiSE-2023

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“…It involves identifying lemmas, which are the base forms of words. For example, "better" would be converted to "good" by a lemmatization algorithm [11].…”

Section: S-5: Lemmatizationmentioning

confidence: 99%

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

Ranjan,

Tiwari,

Sattar

et al. 2024

RAiSE-2023

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“…To retrieve a sequence of a feature vector that refers to an annotation vector ( 21 ), we employ a CNN. The extractor generates Y (vectors); each one is the d-dimensional description of image segmentation, Eq.…”

Section: Mathematical Modelmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

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Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists. By identifying and disambiguating references to biomedical concepts in texts, medical semantics annotators could generate such annotations automatically. For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports). Deep Learning (DL) methods have recently gained much interest for their capacity to achieve expert-level accuracy in automated MedIMG analysis. We discovered that tasks requiring significant responsive fields are ideal for downscaled input images that are qualitatively verified by examining functional, responsive areas and class activating maps for training models. This article focuses on the following contributions: (a) Information Extraction from Narrative MedImages, (b) Automatic categorisation on image resolution with an impact on MedIMG, and (c) Hybrid Model to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose. At the same time, supplying understandable scale weight implies that such multi-scale structures are also crucial for extracting information from high-resolution MedIMG. A portion of the reports (30%) are manually evaluated by trained physicians, while the rest were automatically categorised using deep supervised training models based on attention mechanisms and supplied with test reports. MetaMapLite proved recall and precision, but also an F1-score equivalent for primary biomedicine text search techniques and medical text examination on many databases of MedIMG. In addition to implementing as well as getting the requirements for MedIMG, the article explores the quality of medical data by using DL techniques for reaching large-scale labelled clinical data and also the significance of their real-time efforts in the biomedical study that have played an instrumental role in its extramural diffusion and global appeal.

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“…Knowledge engineering stores and maintains a knowledge base in a structured database format such as UMLS [6]. The rule-based methodology in [28] has been introduced for three types of medical concept extraction: problem, treatment, and test. MetaMap, a medical terminology, has been utilized to extract the semantic features of a concept and then map it, employing rules.…”

Section: Rule-based Approachmentioning

confidence: 99%

“…We compared our proposed system's performance with that of three related systems: QuickUMLS [1], BIO-CRF [28], and the Rules (i2b2) model [22]. The three systems were tested against the i2b2 2010 dataset for three types of concept category extraction (problem, treatment, and test).…”

Section: System Performance Comparison With Competitorsmentioning

confidence: 99%

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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Medical concept extraction: A comparison of statistical and semantic methods

Cited by 6 publications

References 6 publications

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation

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