Text summarization in the biomedical domain: A systematic review of recent research

Mishra, Rashmi; Bian, Jiantao; Fiszman, Marcelo; Weir, Charlene R.; Jonnalagadda, Siddhartha; Mostafa, Javed; Fiol, Guilherme Del

doi:10.1016/j.jbi.2014.06.009

Cited by 176 publications

(133 citation statements)

References 38 publications

(27 reference statements)

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“…From the remaining articles related to the automation of data extraction, it became apparent that MLTs alone are not a sufficient tool. Rather, there should be a symbiosis between machine learning, statistical techniques and, especially, natural language processing which includes the extraction of lexical knowledge, lexical and structural disambiguation (e.g., part of speech tagging, word sense disambiguation), grammatical inference, and robust parsing (Mishra et al, 2014).…”

Section: Classification Methodsmentioning

confidence: 99%

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018Key words: Systematic Reviews, Machine Learning, screening, data extraction, Sensitivity, Specificity Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Reproduction is authorised provided the source is acknowledged. Machine Learning Techniques for Literature and Systematic Reviewswww.efsa.europa.eu/publications 3 EFSA Supporting publication 2018:EN-1427The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract ...

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Section: Classification Methodsmentioning

confidence: 99%

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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“…The approach aims to reduce the source text to express the most important key points through content reduction selection and/or generalization [50]. Although knowledge summarization helps to manage the www.ijacsa.thesai.org information overload, state of the art is still open to research to develop more sophisticated approaches that increase the likelihood of identifying the information.…”

Section: E Biomedical Text Mining Tasksmentioning

confidence: 99%

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Abed¹,

Yuan²,

Li³

2017

ijacsa

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Abstract-The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

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“…Previous efforts to address this problem include methods such as question answering [3] and text summarization. [4]…”

Section: Introductionmentioning

confidence: 99%

“…[3, 4] Yet, consuming the primary literature is labor intensive and not compatible with busy clinical workflows. Rather, clinicians prefer online resources, such as UpToDate and Dynamed, that are written by experts who synthesize the latest clinical evidence on a specific topic.…”

Section: Introductionmentioning

confidence: 99%

“…[4] We propose that, to provide effective CDS, question answering and text summarization tools should focus on extracting clinically actionable recommendations from online information resources, particularly from synthesized resources such as UpToDate. In a previous exploratory study, we tested the feasibility of addressing this problem with a simple semantic-based approach.…”

Section: Introductionmentioning

confidence: 99%

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Classification of clinically useful sentences in clinical evidence resources

Morid

Fiszman

Raja

et al. 2016

Journal of Biomedical Informatics

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Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. Objective To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians’ information needs. Materials and methods We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. Results The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). Conclusions The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier’s model and features used for UpToDate generalized well to Medline abstracts.

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Text summarization in the biomedical domain: A systematic review of recent research

Cited by 176 publications

References 38 publications

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Classification of clinically useful sentences in clinical evidence resources

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