Abstract：
:Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Gopalakrishnan V,Jha K,Jin W,Zhang Adoi
10.1016/j.jbi.2019.103141subject
Has Abstractpub_date
2019-05-01 00:00:00pages
103141eissn
1532-0464issn
1532-0480pii
S1532-0464(19)30059-0journal_volume
93pub_type
杂志文章,评审abstract：:The management of chronic and out-patients is a complex process which requires the cooperation of different agents belonging to several organizational units. Patients have to move to different locations to access the necessary services and to communicate their health status data. From their point of view there should ...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2006.12.002
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abstract：OBJECTIVE:Targeted drugs dramatically improve the treatment outcomes in cancer patients; however, these innovative drugs are often associated with unexpectedly high cardiovascular toxicity. Currently, cardiovascular safety represents both a challenging issue for drug developers, regulators, researchers, and clinicians ...
journal_title：Journal of biomedical informatics
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abstract：:The large amounts of data generated when high-throughput genotyping methods are used in large-scale epidemiological studies (>10,000 participants) present an enormous challenge to researchers in terms of structured data management. In order to face these challenges, a system has been designed and implemented where gen...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2009.07.005
更新日期：2009-12-01 00:00:00
abstract：:The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) wi...
journal_title：Journal of biomedical informatics
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doi：10.1016/j.jbi.2012.01.009
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abstract：:This paper proposes a new framework for the selection of tag SNPs based on haplotypes instead of on a single SNP. The tag SNPs found by this framework form a set of haplotypes completely predictive of the alleles of all untyped SNPs. We refer to this problem as MTMH, which is defined as follows: given a set of SNPs, f...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2008.04.003
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abstract：:Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation,...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2018.04.013
更新日期：2018-06-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
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更新日期：2020-02-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2012.04.013
更新日期：2012-10-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
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更新日期：2020-12-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2005.06.004
更新日期：2006-04-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章,评审
doi：10.1016/j.jbi.2017.12.005
更新日期：2018-02-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/s1532-0464(03)00037-6
更新日期：2002-10-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章,多中心研究
doi：10.1016/j.jbi.2018.03.007
更新日期：2018-05-01 00:00:00
abstract：:Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies give us a 'snapshot' of this disease process over a large number of people but do not allow us to model the temporal ...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2012.11.003
更新日期：2013-04-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/s1532-0464(03)00015-7
更新日期：2002-08-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2019.103342
更新日期：2020-01-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2017.05.009
更新日期：2017-07-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2009.04.004
更新日期：2009-06-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2004.04.002
更新日期：2004-06-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 临床试验,杂志文章,多中心研究
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pub_type： 杂志文章
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journal_title：Journal of biomedical informatics
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2015.05.005
更新日期：2015-08-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2006.02.013
更新日期：2007-02-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2015.01.003
更新日期：2015-04-01 00:00:00
abstract：BACKGROUND:A tool that can predict the estimated glomerular filtration rate (eGFR) in routine daily care can help clinicians to make better decisions for kidney transplant patients and to improve transplantation outcome. In this paper, we proposed a hybrid prediction model for predicting a future value for eGFR during ...
journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2019.103116
更新日期：2019-03-01 00:00:00
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2004.08.001
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journal_title：Journal of biomedical informatics
pub_type： 杂志文章
doi：10.1016/j.jbi.2006.02.004
更新日期：2006-10-01 00:00:00
