An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

Artificial Intelligence in Medicine

Volumn 58, Issue 3, 2013, Pages 185-193

  Stranieri, Andrew ^a   Abawajy, Jemal ^b   Kelarev, Andrei ^b   Huda, Shamsul ^a   Chowdhury, Morshed ^b   Jelinek, Herbert F ^c  

^a UNIVERSITY OF BALLARAT   (Australia)

^b DEAKIN UNIVERSITY   (Australia)

^c CHARLES STURT UNIVERSITY   (Australia)

Author keywords

Accuracy of classification; Cardiac autonomic neuropathy; Decision trees; Diabetes patients; Ewing features; Optimal sequence of tests

Indexed keywords

ACCURACY OF CLASSIFICATIONS; CARDIAC AUTONOMIC NEUROPATHY; DIABETES PATIENTS; EWING FEATURES; OPTIMAL SEQUENCE;

CLASSIFICATION (OF INFORMATION); COST FUNCTIONS; DECISION TREES; FORESTRY; OPTIMIZATION;

STATISTICAL TESTS;

ARTICLE; AUTONOMIC NEUROPATHY; CLINICAL ASSESSMENT TOOL; CLINICAL PRACTICE; COST; DECISION SUPPORT SYSTEM; DECISION TREE; DIABETES MELLITUS; ELECTROCARDIOGRAPHY; EWING TEST; HEART DISEASE; HUMAN; MEASUREMENT ACCURACY; MEDICAL LITERATURE; MEDICAL RESEARCH; PREDICTION; PRIORITY JOURNAL; QRS COMPLEX; SCREENING;

68T05; 68T10; ACCURACY OF CLASSIFICATION; CARDIAC AUTONOMIC NEUROPATHY; DECISION TREES; DIABETES PATIENTS; EWING FEATURES; OPTIMAL SEQUENCE OF TESTS;

ALGORITHMS; BLOOD PRESSURE; BLOOD PRESSURE DETERMINATION; CARDIOVASCULAR DISEASES; DATA MINING; DATABASES, FACTUAL; DECISION SUPPORT TECHNIQUES; DECISION TREES; DIABETIC NEUROPATHIES; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSTIC TECHNIQUES, CARDIOVASCULAR; ELECTROCARDIOGRAPHY; HAND STRENGTH; HEART RATE; HUMANS; PATIENT SELECTION; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; RESPIRATION;

CLASSIFICATION; COSTS; DECISION MAKING; DISEASES; FORESTRY; OPTIMIZATION; STATISTICAL ANALYSIS;

EID: 84880035662     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2013.04.007     Document Type: Article

References (52)

1. Chi C.L., Street N.K., Katz C A decision support system for cost-effective diagnosis Artificial Intelligence in Medicine 2010, 50:149-161.
2. Thompson M.L Assessing the diagnostic accuracy of a sequence of tests Biostatistics 2003, 4:341-351.
3. Oddi A., Cesta A Toward interactive scheduling systems for managing medical resources Artificial Intelligence in Medicine 2000, 20(2):113-138.
4. Houshyar A., Khayyal F.A A mathematical model for scheduling screening tests for progressive diseases Socio-Economic Planning Sciences 1990, 24(3):187-197.
5. Marinagi C.C., Spyropoulos C.D., Papatheodorou C., Kokkotos S Continual planning and scheduling for managing patient tests in hospital laboratories Artificial Intelligence in Medicine 2000, 20(2):139-154.
6. Spyropoulos C.D AI planning and scheduling in the medical hospital environment Artificial Intelligence in Medicine 2000, 20(2):101-111.
7. Bartels J.-H., Zimmermann J Scheduling tests in automotive R&D projects European Journal of Operational Research 2009, 193(3):805-819.
8. Liberatore M.J., Nydick R.L The analytic hierarchy process in medical and health care decision making: a literature review European Journal of Operational Research 2008, 189:194-207.
9. Bellazzi R., Ferrazzi F Predictive data mining in clinical medicine: a focus on selected methods and applications Data Mining and Knowledge Discovery 2011, 1:416-430.
10. Escalante H.J., Montes-y-Gomez M., Gonzalez J.A., Gomez-Gil P., Altamirano L., Reyes C.A., et al Acute leukemia classification by ensemble particle swarm model selection Artificial Intelligence in Medicine 2012, 55(3):163-175.
11. Gagliardi F Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction Artificial Intelligence in Medicine 2011, 52(3):123-139.
12. Kukar M., Kononenko I., Groselj C Modern parameterization and explanation techniques in diagnostic decision support system: a case study in diagnostics of coronary artery disease Artificial Intelligence in Medicine 2011, 52(2):77-90.
13. Liang G., Zhang C Empirical study of bagging predictors on medical data Australasian data mining conference, AusDM 2011, vol 121 of CRPIT 2011, 31-40 ACS, Ballarat, Australia P Vamplew, A Stranieri, K.-L Ong, P Christen, P.J Kennedy (Eds.).
14. Shouman M., Turner T., Stocker R Using decision tree for diagnosing heart disease patients Australasian data mining conference, AusDM 11, vol 121 of CRPIT 2011, 23-30 ACS, Ballarat, Australia P Vamplew, A Stranieri, K.-L Ong, P Christen, P.J Kennedy (Eds.).
15. Van A., Gay V.C., Kennedy P.J., Barin E., Leijdekkers P Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees Australasian data mining conference, AusDM 2011, vol 121 of CRPIT 2011, 11-22 ACS, Ballarat, Australia P Vamplew, A Stranieri, K.-L Ong, P Christen, P.J Kennedy (Eds.).
16. Jelinek H.F., Wilding C., Tinley P An innovative multi-disciplinary diabetes complications screening programme in a rural community: a description and preliminary results of the screening Australian Journal of Primary Health 2006, 12:14-20.
17. Ewing D.J., Campbell J.W., Clarke B.F The natural history of diabetic autonomic neuropathy Quarterly Journal of Medicine 1980, 49:95-100.
18. Ewing D.J., Clarke B.F Diagnosis and management of diabetic autonomic neuropathy British Medical Journal 1982, 285:916-918.
19. Ewing D.J., Martyn C.N., Young R.J., Clarke B.F The value of cardiovascular autonomic function tests: 10 years experience in diabetes Diabetes Care 1985, 8:491-498.
20. Ewing D.J., Campbell J.W., Murray A., Neilson J.M.M., Clarke B.F Immediate heart-rate response to standing: simple test for autonomic neuropathy in diabetes British Medical Journal 1978, 1:145-147.
21. Gonzalez-Clemente J.-M., Vilardell C., Broch M., Megia A., Caixas A., Gimenez-Palop C., et al Lower heart rate variability is associated with higher plasma concentrations of IL-6 in type 1 diabetes European Journal of Endocrinology 2007, 157:31-38.
22. Ewing D.J Which battery of cardiovascular autonomic function tests? Diabetetologia 1990, 33:180-181.
23. Jelinek H.F., Khandoker A., Palaniswami M., McDonald S Heart rate variability and QT dispersion in a cohort of diabetes patients Computing in Cardiology 2010, 37:613-616.
24. Jelinek H.F., Rocha A., Carvalho T., Goldenstein S., Wainer J Machine learning and pattern classification in identification of indigenous retinal pathology 33rd annual international conference of the IEEE Engineering in Medicine and Biology Society 2011, 5951-5954 IEEE Press.
25. Chen H.T., Lin H.D., Wonb J.G.S., Lee C.H., Wu S.C., Lin J.D., et al Cardiovascular autonomic neuropathy, autonomic symptoms and diabetic complications in 674 type 2 diabetes Diabetes Research and Clinical Practice 2008, 82:282-290.
26. Khandoker A.H., Jelinek H.F., Palaniswami M Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis BioMedical Engineering OnLine 2009, 8 http://www.biomedical-engineering-online.com/content/8/1/3.
27. Stella P., Ellis D., Maser R.E., Orcharda T.J Cardiac autonomic neuropathy (expiration and inspiration ratio) in type 1 diabetes incidence and predictors Journal of Diabetes and Its Complications 2000, 14:1-6.
28. Cornforth D., Jelinek H.F Automated classification reveals morphological factors associated with dementia Applied Soft Computing 2007, 8:182-190.
29. Huda S., Jelinek H.F., Ray B., Stranieri A., Yearwood J Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection Sixth international conference on intelligent sensors, sensor networks and information processing, ISSNIP 2010 2010, 297-302 IEEE Press, Sydney.
30. Quinlan R C4.5: programs for machine learning 1993, Morgan Kaufmann, San Mateo, CA.
31. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H The WEKA data mining software: an update SIGKDD Explorations 2009, 11:10-18.
32. Williams G.J Rattle: a data mining GUI for R R Journal 2009, 1:45-55.
33. Williams G Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!) 2011, Springer, New York/Dordrecht/Heidelberg/London.
34. Lamb S.E., McCabe C., Becker C., Fried L.P., Guralnik J The optimal sequence and selection of screening test items to predict fall risk in older disabled women: the women's health and aging study Journal of Gerontology, Series A 2008, 63:1082-1088.
35. Zuzek A., Biasizzo A., Novak F Sequential diagnosis tool Microprocessors and Microsystems 2000, 24:191-197.
36. Chittaro L Information visualization and its application to medicine Artificial Intelligence in Medicine 2001, 22(2):81-88.
37. Combi C., Oliboni B Visually defining and querying consistent multi-granular clinical temporal abstractions Artificial Intelligence in Medicine 2012, 54(2):75-101.
38. Jelinek H.F., Cornforth D.J., Blackmore K Visualisation in biomedicine as a means of data evaluation Journal of Visualization 2011, 14:353-359.
39. Kosara R., Miksch S Metaphors of movement: a visualization and user interface for time-oriented, skeletal plans Artificial Intelligence in Medicine 2001, 22(2):111-131.
40. Plaza L., Diaz A., Gervas P A semantic graph-based approach to biomedical summarisation Artificial Intelligence in Medicine 2011, 53(1):1-14.
41. Tsay J.-J., Wu B.-L., Jeng Y.-S Hierarchically organized layout for visualization of biochemical pathways Artificial Intelligence in Medicine 2010, 48(2-3):107-117.
42. Park C., Godtliebsen F., Taqqu M., Stoev S., Marron J.S Visualization and inference based on wavelet coefficients, SiZer and SiNos Computational Statistics & Data Analysis 2007, 51(12):5994-6012.
43. Park C., Huh J Statistical inference and visualization in scale-space using local likelihood Computational Statistics & Data Analysis 2013, 57(1):336-348.
44. Cho B.H., Yu H., Kim K.-W., Kim T.H., Kim I.Y., Kim S.I Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods Artificial Intelligence in Medicine 2008, 42(1):37-53.
45. May O., Arildsen H Assessing cardiovascular autonomic neuropathy in diabetes mellitis: how many tests to use? Journal of Diabetes and Its Complications 2000, 14:7-12.
46. Shannon C.E A mathematical theory of communication Bell System Technical Journal 1948, 27:379-423.
47. O'Keefe J.H., Hammill S.C., Freed M.S., Pogwizd S.M The complete guide to ECGs 2008, Jones & Bartlett Publishers.
48. Chao P.-K., Wang C.-L., Chan H.-L An intelligent classifier for prognosis of cardiac resynchronization therapy based on speckle-tracking echocardiograms Artificial Intelligence in Medicine 2012, 54(3):181-188.
49. Chen Y.-H., Yu S.-N Selection of effective features for ECG beat recognition based on nonlinear correlations Artificial Intelligence in Medicine 2012, 54(1):43-52.
50. Chiarugi F., Colantonio S., Emmanouilidou D., Martinelli M., Moroni D., Salvetti O Decision support in heart failure through processing of electro- and echocardiograms Artificial Intelligence in Medicine 2010, 50(2):95-104.
51. Gacek A., Pedrycz W A characterization of electrocardiogram signals through optimal allocation of information granularity Artificial Intelligence in Medicine 2012, 54:125-134.
52. Jovic A., Bogunovic N Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features Artificial Intelligence in Medicine 2011, 51(3):175-186.