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Items: 1 to 20 of 137

1.

Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.

Wright A, McCoy AB, Henkin S, Kale A, Sittig DF.

J Am Med Inform Assoc. 2013 Sep-Oct;20(5):887-90. doi: 10.1136/amiajnl-2012-001576. Epub 2013 Mar 30.

3.

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

Rochefort CM, Verma AD, Eguale T, Lee TC, Buckeridge DL.

J Am Med Inform Assoc. 2015 Jan;22(1):155-65. doi: 10.1136/amiajnl-2014-002768. Epub 2014 Oct 20.

4.

Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals.

Connolly B, Matykiewicz P, Bretonnel Cohen K, Standridge SM, Glauser TA, Dlugos DJ, Koh S, Tham E, Pestian J.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):866-70. doi: 10.1136/amiajnl-2013-002601. Epub 2014 Apr 1.

5.

A machine learning-based framework to identify type 2 diabetes through electronic health records.

Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y.

Int J Med Inform. 2017 Jan;97:120-127. doi: 10.1016/j.ijmedinf.2016.09.014. Epub 2016 Oct 1.

PMID:
27919371
6.

Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes.

Lin C, Hsu CJ, Lou YS, Yeh SJ, Lee CC, Su SL, Chen HC.

J Med Internet Res. 2017 Nov 6;19(11):e380. doi: 10.2196/jmir.8344.

7.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

Marafino BJ, Davies JM, Bardach NS, Dean ML, Dudley RA.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):871-5. doi: 10.1136/amiajnl-2014-002694. Epub 2014 Apr 30.

8.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Gobbel GT, Reeves R, Jayaramaraja S, Giuse D, Speroff T, Brown SH, Elkin PL, Matheny ME.

J Biomed Inform. 2014 Apr;48:54-65. doi: 10.1016/j.jbi.2013.11.008. Epub 2013 Dec 4.

9.

A comprehensive study of named entity recognition in Chinese clinical text.

Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):808-14. doi: 10.1136/amiajnl-2013-002381. Epub 2013 Dec 17.

10.

Part-of-speech tagging for clinical text: wall or bridge between institutions?

Fan JW, Prasad R, Yabut RM, Loomis RM, Zisook DS, Mattison JE, Huang Y.

AMIA Annu Symp Proc. 2011;2011:382-91. Epub 2011 Oct 22.

11.
12.

Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application.

Topaz M, Lai K, Dowding D, Lei VJ, Zisberg A, Bowles KH, Zhou L.

Int J Nurs Stud. 2016 Dec;64:25-31. doi: 10.1016/j.ijnurstu.2016.09.013. Epub 2016 Sep 19.

PMID:
27668855
13.

Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

Chen J, Yu H.

J Biomed Inform. 2017 Apr;68:121-131. doi: 10.1016/j.jbi.2017.02.016. Epub 2017 Mar 4.

PMID:
28267590
14.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

Boxwala AA, Kim J, Grillo JM, Ohno-Machado L.

J Am Med Inform Assoc. 2011 Jul-Aug;18(4):498-505. doi: 10.1136/amiajnl-2011-000217.

15.

Word2Vec inversion and traditional text classifiers for phenotyping lupus.

Turner CA, Jacobs AD, Marques CK, Oates JC, Kamen DL, Anderson PE, Obeid JS.

BMC Med Inform Decis Mak. 2017 Aug 22;17(1):126. doi: 10.1186/s12911-017-0518-1.

16.

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.

Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS.

J Biomed Inform. 2016 Apr;60:162-8. doi: 10.1016/j.jbi.2015.12.006. Epub 2015 Dec 17.

17.

Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management.

Garla V, Taylor C, Brandt C.

J Biomed Inform. 2013 Oct;46(5):869-75. doi: 10.1016/j.jbi.2013.06.014. Epub 2013 Jul 8.

18.

Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches.

Wu J, Roy J, Stewart WF.

Med Care. 2010 Jun;48(6 Suppl):S106-13. doi: 10.1097/MLR.0b013e3181de9e17.

PMID:
20473190
19.

Finding falls in ambulatory care clinical documents using statistical text mining.

McCart JA, Berndt DJ, Jarman J, Finch DK, Luther SL.

J Am Med Inform Assoc. 2013 Sep-Oct;20(5):906-14. doi: 10.1136/amiajnl-2012-001334. Epub 2012 Dec 15.

20.

Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

Marella WM, Sparnon E, Finley E.

J Patient Saf. 2017 Mar;13(1):31-36. doi: 10.1097/PTS.0000000000000104.

PMID:
24721977

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