PUBLICATION DATE

2015-09-28

AUTHORS

Gang Luo

TITLE

MLBCD: a machine learning tool for big clinical data

ISSUE

1

VOLUME

3

ISSN (print)

N/A

ISSN (electronic)

2047-2501

ABSTRACT

BackgroundPredictive modeling is fundamental for extracting value from large clinical data sets, or “big clinical data,” advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. MethodsThis paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. ResultsThe paper describes MLBCD’s design in detail. ConclusionsBy making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

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33 TRIPLES      31 PREDICATES      32 URIs      20 LITERALS

Subject Predicate Object
1 articles:c7a5b6326c62a49016b85bd764a84fe8 sg:abstract Abstract BackgroundPredictive modeling is fundamental for extracting value from large clinical data sets, or “big clinical data,” advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. MethodsThis paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. ResultsThe paper describes MLBCD’s design in detail. ConclusionsBy making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.
2 sg:articleType OriginalPaper
3 sg:coverYear 2015
4 sg:coverYearMonth 2015-12
5 sg:ddsId s13755-015-0011-0
6 sg:ddsIdJournalBrand 13755
7 sg:doi 10.1186/s13755-015-0011-0
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17 sg:issnElectronic 2047-2501
18 sg:issue 1
19 sg:language English
20 sg:license http://creativecommons.org/licenses/by/4.0/
21 http://scigraph.springernature.com/explorer/license/
22 sg:openAccess OpenAccess/CC BY + CC0/4.0
23 sg:pageEnd 19
24 sg:pageStart 1
25 sg:publicationDate 2015-09-28
26 sg:publicationYear 2015
27 sg:publicationYearMonth 2015-09
28 sg:scigraphId c7a5b6326c62a49016b85bd764a84fe8
29 sg:title MLBCD: a machine learning tool for big clinical data
30 sg:volume 3
31 sg:webpage https://link.springer.com/10.1186/s13755-015-0011-0
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33 rdfs:label Article: MLBCD: a machine learning tool for big clinical data
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