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Items: 1 to 20 of 75

1.

Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records.

Afzal Z, Schuemie MJ, van Blijderveen JC, Sen EF, Sturkenboom MC, Kors JA.

BMC Med Inform Decis Mak. 2013 Mar 2;13:30. doi: 10.1186/1472-6947-13-30.

2.

Automating classification of free-text electronic health records for epidemiological studies.

Schuemie MJ, Sen E, 't Jong GW, van Soest EM, Sturkenboom MC, Kors JA.

Pharmacoepidemiol Drug Saf. 2012 Jun;21(6):651-8. doi: 10.1002/pds.3205. Epub 2012 Jan 24.

PMID:
22271492
3.

Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases.

Afzal Z, Engelkes M, Verhamme KM, Janssens HM, Sturkenboom MC, Kors JA, Schuemie MJ.

Pharmacoepidemiol Drug Saf. 2013 Aug;22(8):826-33. doi: 10.1002/pds.3438. Epub 2013 Apr 17.

PMID:
23592573
4.

Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance.

Klompas M, Haney G, Church D, Lazarus R, Hou X, Platt R.

PLoS One. 2008 Jul 9;3(7):e2626. doi: 10.1371/journal.pone.0002626.

5.
6.

Automated outcome classification of emergency department computed tomography imaging reports.

Yadav K, Sarioglu E, Smith M, Choi HA.

Acad Emerg Med. 2013 Aug;20(8):848-54. doi: 10.1111/acem.12174.

7.

Automatic de-identification of textual documents in the electronic health record: a review of recent research.

Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH.

BMC Med Res Methodol. 2010 Aug 2;10:70. doi: 10.1186/1471-2288-10-70. Review.

8.

De-identification of primary care electronic medical records free-text data in Ontario, Canada.

Tu K, Klein-Geltink J, Mitiku TF, Mihai C, Martin J.

BMC Med Inform Decis Mak. 2010 Jun 18;10:35. doi: 10.1186/1472-6947-10-35.

9.

Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.

Wang Z, Shah AD, Tate AR, Denaxas S, Shawe-Taylor J, Hemingway H.

PLoS One. 2012;7(1):e30412. doi: 10.1371/journal.pone.0030412. Epub 2012 Jan 19.

10.

Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans.

Martinez D, Ananda-Rajah MR, Suominen H, Slavin MA, Thursky KA, Cavedon L.

J Biomed Inform. 2015 Feb;53:251-60. doi: 10.1016/j.jbi.2014.11.009. Epub 2014 Nov 24.

11.

Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy.

DeLisle S, Kim B, Deepak J, Siddiqui T, Gundlapalli A, Samore M, D'Avolio L.

PLoS One. 2013 Aug 13;8(8):e70944. doi: 10.1371/journal.pone.0070944. eCollection 2013.

12.

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.

Judson R, Elloumi F, Setzer RW, Li Z, Shah I.

BMC Bioinformatics. 2008 May 19;9:241. doi: 10.1186/1471-2105-9-241.

13.

Using machine learning classifiers to assist healthcare-related decisions: classification of electronic patient records.

Pollettini JT, Panico SR, Daneluzzi JC, TinĂ³s R, Baranauskas JA, Macedo AA.

J Med Syst. 2012 Dec;36(6):3861-74. doi: 10.1007/s10916-012-9859-6. Epub 2012 May 18.

PMID:
22592391
14.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

Ozcift A, Gulten A.

Comput Methods Programs Biomed. 2011 Dec;104(3):443-51. doi: 10.1016/j.cmpb.2011.03.018. Epub 2011 Apr 30.

PMID:
21531475
15.

Combining free text and structured electronic medical record entries to detect acute respiratory infections.

DeLisle S, South B, Anthony JA, Kalp E, Gundlapallli A, Curriero FC, Glass GE, Samore M, Perl TM.

PLoS One. 2010 Oct 14;5(10):e13377. doi: 10.1371/journal.pone.0013377.

16.

Screening nonrandomized studies for medical systematic reviews: a comparative study of classifiers.

Bekhuis T, Demner-Fushman D.

Artif Intell Med. 2012 Jul;55(3):197-207. doi: 10.1016/j.artmed.2012.05.002. Epub 2012 Jun 5.

17.

Active learning strategies for the deduplication of electronic patient data using classification trees.

Sariyar M, Borg A, Pommerening K.

J Biomed Inform. 2012 Oct;45(5):893-900. doi: 10.1016/j.jbi.2012.02.002. Epub 2012 Feb 28.

18.

Electronic medical records for clinical research: application to the identification of heart failure.

Pakhomov S, Weston SA, Jacobsen SJ, Chute CG, Meverden R, Roger VL.

Am J Manag Care. 2007 Jun;13(6 Part 1):281-8.

19.

Positive-unlabeled learning for disease gene identification.

Yang P, Li XL, Mei JP, Kwoh CK, Ng SK.

Bioinformatics. 2012 Oct 15;28(20):2640-7. doi: 10.1093/bioinformatics/bts504. Epub 2012 Aug 24.

20.

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease.

Amaral JL, Lopes AJ, Jansen JM, Faria AC, Melo PL.

Comput Methods Programs Biomed. 2012 Mar;105(3):183-93. doi: 10.1016/j.cmpb.2011.09.009. Epub 2011 Oct 21.

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