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Identifying Patterns in Medical Records through Latent Semantic Analysis


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Mountains of data are constantly being accumulated, including in the form of medical records of doctor visits and treatments. The question is what actionable information can be gleaned from it beyond a one-time record of a specific medical examination. Arguably, if one were to combine the data in a large corpus of many patients suffering from the same condition, then overall patterns that apply beyond a specific instance of a specific doctor visit might be observed. Such patterns might reveal how medical conditions are related to one another over a broad set of patients, as well as how these conditions might be related to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) of the Centers for Disease Control and Prevention (CDC) Classification of Diseases, Functioning, and Disability codes (henceforth, ICD codesa). Conceivably, applying such a method to a large dataset could even suggest new avenues of medical and public health research by identifying new associations, along with the relative strength of the associations compared to other associations. It might even be applied to identify possible side effects in phase IV drug testing. Moreover, that potential might be even more potent if it could also identify indirect, or through other terms, connections among terms.

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To address such medical-analysis objectives, this article explores in a preliminary manner the applicability of a method based on latent semantic analysis (LSA) that transforms the term-document [frequency] matrix (TDM) through a singular value decomposition (SVD) into a semantic space. Once such a semantic space is created, the lexical distance among terms and among combinations of terms can be calculated by projecting them onto that space. More important, lexical distance can be calculated even when terms are associated only indirectly; that is, when the terms do not appear together in any one shared document but are related to one another through another set of terms;10,12 see Gefen et al.4 and Holzinger et al.8 for discussions of other text-analysis methods.


Comments


Michael Berry

I thought I would provide additional references on LSA that will help readers with the underlying mathematics and usage of LSA in bioinformatics for generating text-based similarity scores similar to p-values from gene expression data. Software for LSA has been available in languages such as C, C++, Java, and Python for many years. M.W. Berry (EECS Department, Univ of Tennessee, Knoxville)

LSA references:

Knowledge-Enhanced Latent Semantic Indexing, D. Guo, M.W. Berry, B.B.
Thompson, and S. Bailin, Information Retrieval 6(2), 2003, pp. 225-250.

Mathematical Foundations Behind Latent Semantic Analysis, D.I. Martin
and M.W. Berry, in Handbook of Latent Semantic Analysis, T.K. Landauer,
D.S. McNamara, S. Dennis, and W. Kintsch (Eds), Lawrence Erlbaum Associates,
2007, pp. 35-55.

Latent Semantic Indexing, Dian I. Martin and Michael W. Berry, in Encyclopedia
of Library and Information Sciences (ELIS), Third Edition, Marcia J. Bates
and Mary Niles Maack (Eds.), Taylor & Francis, Oxford, 2010, pp. 3195-3204.

Latent Semantic Indexing of Pubmed Abstracts for Identification of Transcription
Factor Candidates from Microarray-derived Gene Sets, S. Roy, K. Heinrich,
V. Phan, M.W. Berry, and R. Homayouni, BMC Bioinformatics 12(Suppl 10):S19,
2011.

Functional Cohesion of Gene Sets Determined by Latent Semantic Indexing
of PubMed Abstracts, L. Xu, N. Furlotte, E.O. George, K. Heinrich, M.W.
Berry, and R. Homayouni, PLoS ONE, 6(4): e18851, 2011.


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