DiseaSE: A biomedical text analytics system for disease symptom extraction and characterization

Abulaish, Muhammad; Parwez, Md. Aslam; Jahiruddin,

doi:10.1016/j.jbi.2019.103324

Cited by 17 publications

(26 citation statements)

References 8 publications

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“…Studies in this review relied primarily on MEDLINE and PubMed. MEDLINE is a US National Library of Medicine Gurulingappa et al (2013), Gong et al (2016), Kang et al (2014), Yeleswarapu et al (2014), Kolchinsky et al (2015), Sang et al (2019), Xu and Wang (2014) and Abulaish and Parwez (2019). Besides, Su and Sanger (2017) analyzed the fast-growing data in clinical trial documents from ClinicalTrials.gov to devise a novel method of drug repositioning.…”

Section: Biomedical Literaturementioning

confidence: 99%

“…Machine learning techniques have also been used in different studies to infer relationships between entities or for classification (Gurulingappa et al, 2013;Torii et al, 2015;Garla et al, 2013;Cormack et al, 2015;Sadeque et al, 2017;Lee et al, 2015;Pereira et al, 2013;Wu et al, 2020;Sedghi et al, 2015;Shah et al, 2019;Amrit et al, 2016;Mehta et al, 2020). Feasibility analysis is a technique used by Abulaish and Parwez (2019) to assess the feasibility of disease-symptom association after these have been extracted by their system. In their approach, Gurulingappa et al (2013) have used Java simple relationship extraction to detect and classify relations between drugs and adverse events from case reports.…”

Section: Web Search Logsmentioning

confidence: 99%

“…In their proposed framework for identifying ADEs from patient social media, Liu and Chen (2015) used MetaMap to map social media text to UMLS, by extracting drug and event names. MetaMap has also been used for early depression detection (Sadeque et al, 2017), detection of negative and uncertain findings in radiology reports (Peng et al, 2018), mining of ADE mentions from social media (Nikfarjam et al, 2015) and mining the associations between diseases and symptoms (Shah et al, 2019;Abulaish and Parwez, 2019).…”

Section: Metamapmentioning

confidence: 99%

“…Xu and Wang (2014) syntactically parsed sentences in MEDLINE articles with the Stanford parser using Amazon cloud computing service for better performance. The Stanford parser has also been used in the text mining system proposed by Abulaish and Parwez (2019) for POS tagging.…”

Section: Stanford Parsermentioning

confidence: 99%

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A systematic review of text mining approaches applied to various application areas in the biomedical domain

Cheerkoot-Jalim

Khedo

2020

JKM

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Purpose This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed. Design/methodology/approach The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted. Findings It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums. Originality/value To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

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Section: Biomedical Literaturementioning

confidence: 99%

Section: Web Search Logsmentioning

confidence: 99%

Section: Metamapmentioning

confidence: 99%

Section: Stanford Parsermentioning

confidence: 99%

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A systematic review of text mining approaches applied to various application areas in the biomedical domain

Cheerkoot-Jalim

Khedo

2020

JKM

View full text Add to dashboard Cite

show abstract

“…Text mining uses natural language processing (NLP) for unstructured data, such as the text of abstracts or full-text papers, to summarize information using various methods ( Zengul et al, 2020 ; Zhao et al, 2018 ). This body of methodologies provides multiple ways to analyze unstructured data including visualization through word clouds and knowledge maps, and ranking top ckeywords, phrases, and sentences by importance ( Abulaish, Parwez, & Jahiruddin, 2019 ; Garg & Kumar, 2018 ; Yang et al, 2018 ; Zhao et al, 2018 ). NLP is a type of text mining that scans through large amounts of unstructured data to extract meaning and information from the data.…”

Section: Introductionmentioning

confidence: 99%

The nurse COVID and historical epidemics literature repository: Development, description, and summary

et al. 2021

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Investigating Clinical Named Entity Recognition Approaches for Information Extraction from EMR

Mahajan

Rana

2021

Intelligent Systems Reference Library

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DiseaSE: A biomedical text analytics system for disease symptom extraction and characterization

Cited by 17 publications

References 8 publications

A systematic review of text mining approaches applied to various application areas in the biomedical domain

A systematic review of text mining approaches applied to various application areas in the biomedical domain

The nurse COVID and historical epidemics literature repository: Development, description, and summary

Investigating Clinical Named Entity Recognition Approaches for Information Extraction from EMR

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