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Synthesizer: Expediting synthesis studies from context-free data with natural language processing

View ORCID ProfileLisa Gandy, Jordan Gumm, Benjamin Fertig, Michael J. Kennish, Sameer Chavan, View ORCID ProfileAnn Thessen, Luigi Marchionni, Xiaoxan Xia, Shambhavi Shankrit, Elana J Fertig
doi: https://doi.org/10.1101/053629
Lisa Gandy
1Central Michigan University
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Jordan Gumm
1Central Michigan University
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Benjamin Fertig
2University of Maryland College Park
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Michael J. Kennish
3Rutgers University
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Sameer Chavan
4University of Colorado Denver
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Ann Thessen
5Ronan Institute for Independent Scholarship
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Luigi Marchionni
6Johns Hopkins University School of Medicine
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Xiaoxan Xia
6Johns Hopkins University School of Medicine
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Shambhavi Shankrit
6Johns Hopkins University School of Medicine
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Elana J Fertig
6Johns Hopkins University School of Medicine
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Abstract

Today’s low cost digital data provides unprecedented opportunities for scientific discovery from synthesis studies. For example, the medical field is revolutionizing patient care by creating personalized treatment plans based upon mining electronic medical records, imaging, and genomics data. Standardized annotations are essential to subsequent analyses for synthesis studies. However, accurately combining records from diverse studies requires tedious and error-prone human curation, posing a significant barrier to synthesis studies. We propose a novel natural language processing (NLP) algorithm, Synthesize, to merge data annotations automatically. Application to patient characteristics for diverse human cancers and ecological datasets demonstrates the accuracy of Synthesize in diverse scientific disciplines. This NLP approach is implemented in an open-source software package, Synthesizer. Synthesizer is a generalized, user-friendly system for error-free data merging.

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Posted May 16, 2016.
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Synthesizer: Expediting synthesis studies from context-free data with natural language processing
Lisa Gandy, Jordan Gumm, Benjamin Fertig, Michael J. Kennish, Sameer Chavan, Ann Thessen, Luigi Marchionni, Xiaoxan Xia, Shambhavi Shankrit, Elana J Fertig
bioRxiv 053629; doi: https://doi.org/10.1101/053629
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Synthesizer: Expediting synthesis studies from context-free data with natural language processing
Lisa Gandy, Jordan Gumm, Benjamin Fertig, Michael J. Kennish, Sameer Chavan, Ann Thessen, Luigi Marchionni, Xiaoxan Xia, Shambhavi Shankrit, Elana J Fertig
bioRxiv 053629; doi: https://doi.org/10.1101/053629

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