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Items: 1 to 20 of 87

1.

Exploring generalized association rule mining for disease co-occurrences.

Kost R, Littenberg B, Chen ES.

AMIA Annu Symp Proc. 2012;2012:1284-93.

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Automatic construction of rule-based ICD-9-CM coding systems.

Farkas R, Szarvas G.

BMC Bioinformatics. 2008 Apr 11;9 Suppl 3:S10. doi: 10.1186/1471-2105-9-S3-S10.

4.

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA.

Med Care. 2005 Nov;43(11):1130-9.

PMID:
16224307
5.

Cross-Ontology multi-level association rule mining in the Gene Ontology.

Manda P, Ozkan S, Wang H, McCarthy F, Bridges SM.

PLoS One. 2012;7(10):e47411. doi: 10.1371/journal.pone.0047411.

6.

Discovering metric temporal constraint networks on temporal databases.

Álvarez MR, Félix P, Cariñena P.

Artif Intell Med. 2013 Jul;58(3):139-54. doi: 10.1016/j.artmed.2013.03.006.

PMID:
23660232
7.

Compass: a hybrid method for clinical and biobank data mining.

Krysiak-Baltyn K, Nordahl Petersen T, Audouze K, Jørgensen N, Angquist L, Brunak S.

J Biomed Inform. 2014 Feb;47:160-70. doi: 10.1016/j.jbi.2013.10.007.

8.

A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance.

Ji Y, Ying H, Dews P, Mansour A, Tran J, Miller RE, Massanari RM.

IEEE Trans Inf Technol Biomed. 2011 May;15(3):428-37. doi: 10.1109/TITB.2011.2131669.

PMID:
21435986
9.

Conceptual-driven classification for coding advise in health insurance reimbursement.

Li ST, Chen CC, Huang F.

Artif Intell Med. 2011 Jan;51(1):27-41. doi: 10.1016/j.artmed.2010.10.003.

PMID:
21129939
10.

Negative and positive association rules mining from text using frequent and infrequent itemsets.

Mahmood S, Shahbaz M, Guergachi A.

ScientificWorldJournal. 2014;2014:973750. doi: 10.1155/2014/973750.

11.

A primer to frequent itemset mining for bioinformatics.

Naulaerts S, Meysman P, Bittremieux W, Vu TN, Vanden Berghe W, Goethals B, Laukens K.

Brief Bioinform. 2015 Mar;16(2):216-31. doi: 10.1093/bib/bbt074. Review.

12.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data.

Agapito G, Guzzi PH, Cannataro M.

J Biomed Inform. 2015 Aug;56:273-83. doi: 10.1016/j.jbi.2015.06.005.

13.

Mining rare associations between biological ontologies.

Benites F, Simon S, Sapozhnikova E.

PLoS One. 2014 Jan 3;9(1):e84475. doi: 10.1371/journal.pone.0084475. Erratum in: PLoS One. 2014;9(7):e103663.

14.

Implementation of ICD-10 in Canada: how has it impacted coded hospital discharge data?

Walker RL, Hennessy DA, Johansen H, Sambell C, Lix L, Quan H.

BMC Health Serv Res. 2012 Jun 10;12:149. doi: 10.1186/1472-6963-12-149.

15.

Applying MetaMap to Medline for identifying novel associations in a large clinical dataset: a feasibility analysis.

Hanauer DA, Saeed M, Zheng K, Mei Q, Shedden K, Aronson AR, Ramakrishnan N.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):925-37. doi: 10.1136/amiajnl-2014-002767.

16.

Comorbidity study on type 2 diabetes mellitus using data mining.

Kim HS, Shin AM, Kim MK, Kim YN.

Korean J Intern Med. 2012 Jun;27(2):197-202. doi: 10.3904/kjim.2012.27.2.197.

17.

DISEASES: text mining and data integration of disease-gene associations.

Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ.

Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020.

18.

New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality.

Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA.

J Clin Epidemiol. 2004 Dec;57(12):1288-94.

PMID:
15617955
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20.

Effect of temporal relationships in associative rule mining for web log data.

Khairudin NM, Mustapha A, Ahmad MH.

ScientificWorldJournal. 2014 Jan 23;2014:813983. doi: 10.1155/2014/813983.

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