Naive Bayes classifiers for verbal autopsies: Comparison to physician-based classification for 21,000 child and adult deaths

BMC Medicine

Volumn 13, Issue 1, 2015, Pages

  Miasnikof, Pierre ^a   Giannakeas, Vasily ^a   Gomes, Mireille ^a   Aleksandrowicz, Lukasz ^a   Shestopaloff, Alexander Y ^b   Alam, Dewan ^a,c   Tollman, Stephen ^d   Samarikhalaj, Akram ^a,e   Jha, Prabhat ^a,b  

^a ST MICHAEL'S HOSPITAL   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c INTERNATIONAL CENTRE FOR DIARRHOEAL DISEASE RESEARCH   (Bangladesh)

^d UNIVERSITY OF THE WITWATERSRAND   (South Africa)

^e RYERSON UNIVERSITY   (Canada)

Author keywords

Cause of death (COD); Computer coded verbal autopsy (CCVA); InterVA; Naive Bayes classifier; Physician certified verbal autopsy (PCVA); Tariff; Verbal autopsy (VA)

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; BAYESIAN LEARNING; CAUSE OF DEATH; CHILD; CLASSIFICATION ALGORITHM; COMPARATIVE STUDY; CONTROLLED STUDY; HUMAN; INFANT; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MEASUREMENT ACCURACY; PHYSICIAN; SENSITIVITY AND SPECIFICITY; VERBAL AUTOPSY; AUTOPSY; BAYES THEOREM; DEATH CERTIFICATE; DEMOGRAPHY; EPIDEMIOLOGY; INCOME; INDIA; MIDDLE AGED; NEWBORN; PRESCHOOL CHILD; SOUTH AFRICA; STANDARDS; STATISTICS AND NUMERICAL DATA; YOUNG ADULT;

ADOLESCENT; ADULT; AUTOPSY; BAYES THEOREM; CAUSE OF DEATH; CHILD; CHILD, PRESCHOOL; DEATH CERTIFICATES; DEMOGRAPHY; HUMANS; INCOME; INDIA; INFANT; INFANT, NEWBORN; MIDDLE AGED; PHYSICIANS; SOUTH AFRICA; YOUNG ADULT;

EID: 84947901793     PISSN: None     EISSN: 17417015     Source Type: Journal    
DOI: 10.1186/s12916-015-0521-2     Document Type: Article

References (33)

1. Jha P. Counting the dead is one of the world's best investments to reduce premature mortality. Hypothesis. 2012;10(1):e3. doi: 10.5779/hypothesis.v5710i5771.5254.
2. Setel PW, Sankoh O, Rao C, Velkoff VA, Mathers C, Gonghuan Y, et al. Sample registration of vital events with verbal autopsy: a renewed commitment to measuring and monitoring vital statistics. Bull World Health Organ. 2005;83(8):611-7.
3. Fottrell E, Byass P. Verbal autopsy: methods in transition. Epidemiol Rev. 2010;32(1):38-55.
4. Aleksandrowicz L, Malhotra V, Dikshit R, Gupta PC, Kumar R, Sheth J, et al. Performance criteria for verbal autopsy-based systems to estimate national causes of death: development and application to the Indian Million Death Study. BMC Med. 2014;12:21.
5. Murray CJ, Lozano R, Flaxman AD, Serina P, Phillips D, Stewart A, et al. Using verbal autopsy to measure causes of death: the comparative performance of existing methods. BMC Med. 2014;12:5.
6. Chandramohan D. Validation and validity of verbal autopsy procedures. Popul Health Metr. 2011;9:22.
7. James SL, Flaxman AD, Murray CJ. Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies. Popul Health Metr. 2011;9:31.
8. Desai N, Aleksandrowicz L, Miasnikof P, Byass P, Tollman S, Alam D, et al. Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries. BMC Med. 2014;12:20.
9. Garenne M. Prospects for automated diagnosis of verbal autopsies. BMC Med. 2014;12:18.
10. Rish I. An empirical study of the naive Bayes classifier. Technical Report RC22230, IBM T.J. Watson Research Center; 2001.
11. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York, USA: Springer; 2009.
12. Bayes T, Price R. An essay towards solving a problem in the doctrine of chances: by the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, M.A. and F.R.S. Philos Trans R Soc Lond. 1763;53:370-418.
13. Danso S, Atwell E, Johnson O. A comparative study of machine learning methods for verbal autopsy text classification. Int J Comput Sci Issues. 2013;10(2):47-60.
14. Hailemariam T. Application of data mining for predicting adult mortality. Master's thesis. Addis Ababa, Ethiopia: Addis Ababa University; 2012.
15. Tekabe B. Predicting the pattern of under-five mortality in Ethiopia using data mining technology: the case of Butajira Rural Health Program. Master's thesis. Addis Ababa, Ethiopia: Addis Ababa University, 2012.
16. Byass P, Chandramohan D, Clark SJ, D'Ambruoso L, Fottrell E, Graham WJ, et al. Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool. Glob Health Action. 2012;5:1-8.
17. Jha P, Gajalakshmi V, Gupta PC, Kumar R, Mony P, Dhingra N, et al. Prospective study of one million deaths in India: rationale, design, and validation results. PLoS Med. 2006;3(2):e18.
18. Kahn K, Collinson MA, Gomez-Olive FX, Mokoena O, Twine R, Mee P, et al. Profile: Agincourt health and socio-demographic surveillance system. Int J Epidemiol. 2012;41(4):988-1001.
19. Health and Demographic Surveillance System. Matlab. Volume 36: Registration of Health and Demographic Events 2003. Scientific Report No. 92. Dhaka: ICDDR,B; 2005.
20. Byass P. Usefulness of the population health metrics research consortium gold standard verbal autopsy data for general verbal autopsy methods. BMC Med. 2014;12:23.
21. Alam DS, Jha P, Ramasundarahettige C, Streatfield PK, Niessen LW, Chowdhury MA, et al. Smoking-attributable mortality in Bangladesh: proportional mortality study. Bull World Health Organ. 2013;91(10):757-64.
22. Murray CJ, Lozano R, Flaxman AD, Vahdatpour A, Lopez AD. Robust metrics for assessing the performance of different verbal autopsy cause assignment methods in validation studies. Popul Health Metr. 2011;9:28.
23. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014.
24. Flaxman AD, Vahdatpour A, James SL, Birnbaum JK, Murray CJ. Direct estimation of cause-specific mortality fractions from verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metr. 2011;9:35.
25. King G, Lu Y. Verbal autopsy methods with multiple causes of death. Stat Sci. 2008;23:78-91.
26. Byass P, Herbst K, Fottrell E, Ali MM, Odhiambo F, Amek N, et al. Comparing verbal autopsy cause of death findings as determined by physician coding and probabilistic modelling: a public health analysis of 54 000 deaths in Africa and Asia. J Glob Health. 2015;5(1):010402.
27. McCormick T, Li Z, Calvert C, Crampin A, Kahn K, Clark S. Probabilstic cause-of-death assignment using verbal autopsies. Available: http://arxiv.org/pdf/1411.3042v2.pdf. In press.
28. AbouZahr C, de Savigny D, Mikkelsen L, Setel PW, Lozano R, Lopez AD. Towards universal civil registration and vital statistics systems: the time is now. Lancet. 2015;386(1000):1407-18.
29. Hill K, Lopez AD, Shibuya K, Jha P. Interim measures for meeting needs for health sector data: births, deaths, and causes of death. Lancet. 2007;370(9600):1726-35.
30. Bloomberg Philanthropies. Data for Health. http://www.bloomberg.org/program/public-health/data-health/. Accessed on 17/11/2015.
31. Jha P. Reliable direct measurement of causes of death in low and middle-income countries. BMC Med. 2013;12:19.
32. Byass P, de Savigny D, Lopez AD. Essential evidence for guiding health system priorities and policies: anticipating epidemiological transition in Africa. Glob Health Action. 2014;7:23359.
33. Flaxman AD, Serina P, Stewart A, James SL, Vahdatpour A, Hernandez B, et al. Ensemble modelling in verbal autopsy: the popular voting method. Lancet. 2013: 381 Suppl 2:S48.